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The Journal of Immunology, 2002, 169: 522-530.
Copyright © 2002 by The American Association of Immunologists

Analysis of the Innate and Adaptive Phases of Allograft Rejection by Cluster Analysis of Transcriptional Profiles1

Kenneth Christopher2, Thomas F. Mueller2, Chunyan Ma, Yurong Liang and David L. Perkins3

Laboratory of Molecular Immunology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Both clinical and experimental observations suggest that allograft rejection is a complex process with multiple components that are, at least partially, functionally redundant. Studies using graft recipients deficient in various genes including chemokines, cytokines, and other immune-associated genes frequently produce a phenotype of delayed, but not indefinitely prevented, rejection. Only a small subset of genetic deletions (for example, TCR{alpha} or {beta}, MHC I and II, B7-1 and B7-2, and recombinase-activating gene) permit permanent graft acceptance suggesting that rejection is orchestrated by a complex network of interrelated inflammatory and immune responses. To investigate this complex process, we have used oligonucleotide microarrays to generate quantitative mRNA expression profiles following transplantation. Patterns of gene expression were confirmed with real-time PCR data. Hierarchical clustering algorithms clearly differentiated the early and late phases of rejection. Self-organizing maps identified clusters of coordinately regulated genes. Genes up-regulated during the early phase included genes with prior biological functions associated with ischemia, injury, and Ag-independent innate immunity, whereas genes up-regulated in the late phase were enriched for genes associated with adaptive immunity.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Multiple types of evidence indicate that allograft rejection is a complex process involving a diverse array of proinflammatory and immune responses. In clinical transplantation, despite the use of multiple immunosuppressive agents, the incidence of both acute and chronic rejection remains a significant problem (1, 2, 3, 4, 5, 6). The standard requirement for multiple immunosuppressive agents is consistent with the notion of a complex immune response involving multiple components. Studies in animal models are also consistent with this concept. For example, in the murine heterotopic heart transplant model, many studies of knockout strains deficient in various chemokines, cytokines, inflammatory molecules, and receptors have been published showing a similar phenotype of delayed, but not totally prevented, rejection. The common observation in these studies of gene-deficient mice that rejection is delayed, but not indefinitely prevented, supports a model in which multiple different components of the alloimmune response are important, but not always necessary, to induce graft rejection. Only in a few gene-deficient models, such as elimination of all T cells or both B7-1 and B7-2 costimulatory molecules, and probably the deletion of both MHC class I and II, is the graft permanently accepted without rejection (7, 8, 9, 10, 11, 12).

Additional complexity is generated by contributions from both innate and adaptive immunity to the process of allograft rejection. Previous results from our laboratory analyzing proinflammatory responses in graft recipients deficient in T and B lymphocytes have characterized a robust innate immune response that occurs during the first 24 h following transplantation (13). Importantly, the innate response included multiple proinflammatory molecules including chemokines, cytokines, and receptors. These observations suggested that, similar to infectious models, the early phase of rejection consists predominantly of innate immune responses, whereas the late phases of rejection are enriched for components of adaptive immunity. In the current study, our objective was to analyze the early and late phases of rejection to investigate the contributions of the innate and adaptive immune responses.

Although the notion that the alloimmune response is complex is commonly accepted, experimental approaches have typically analyzed a small subset of parameters following a single manipulation such as administration of an immune modulator or deletion of a gene. With the development of DNA microarray technology, the ability to produce databases including expression profiles of large numbers of mRNAs has become practical. In contrast to hypothesis driven approaches that focus on a single mechanism in a network of biological complexity, microarray experiments attempt to globally describe gene expression. With the completion of the human and murine genome sequences, it is becoming feasible to monitor the expression of all genes. Such global approaches have already been successfully applied to simpler organisms such as yeast (14, 15). One obvious limitation of microarray studies is that data is limited to RNA, but not protein, expression. However, by analyzing a large dataset of mRNA expression, previous reports have detected the perturbation of biological responses by detecting changes in gene expression despite the fact that a subset of the genes in the network may be regulated posttranscriptionally. A recent single study in yeast shows that microarray data can corroborate, and even expand, our understanding of metabolic pathways or transcriptional regulation by DNA binding proteins decoded by decades of biochemical studies (16, 17). Interestingly, in this yeast study, only 5% of genes are regulated independently of transcription (17). In our study, we linked physiological outcomes (allograft rejection) with changes in gene expression (determined by microarrays). Our assumption was that the combination of biological function with gene expression data from a kinetic analysis would characterize the innate and adaptive phases of graft rejection.

A major challenge of microarray studies is the meaningful interpretation of the huge databases of expression values. In our studies, hierarchical clustering algorithms were used to distinguish various experimental groups (15). These algorithms successfully differentiated different tissues, different time points, and rejection from the control grafts. In addition, to identify specific subsets of coordinately regulated genes in the alloimmune response, we used self-organizing maps (SOM)4 to detect clusters of genes that were coordinately regulated (18). Our results combine biologic function with gene expression profiles to differentiate the early response, which includes genes associated with ischemia, injury, and components of innate immunity, from the late response, which includes genes enriched for components of adaptive immunity.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Vascularized heterotopic cardiac transplantation

Eight- to 12-wk-old male mice, including BALB/cByJ (BALB/c) (H-2d), C57BL/6J (B6) (H-2b), C57BL/6J-Rag-1tm1Mom (B6-Rag) (H-2b) (The Jackson Laboratory, Bar Harbor, ME), and BALB/c-AnNTac-Rag2tm1N12 (BALB/c-Rag) (H-2d) (Taconic Farms, Germantown, NY), were used as donors and recipients in the transplant experiments. As previously described (13), hearts were harvested from donors and immediately transplanted into 8- to 12-wk-old recipients which were anesthetized via i.p. injection with 60 µg/kg of pentobarbital sodium. The donor aorta was attached to the recipient abdominal aorta by end to side anastomosis, and the donor pulmonary artery was attached to the recipient vena cava by end to side anastomosis. All surgical procedures were completed in <60 min from the time that the donor heart was harvested. Donor hearts that did not beat immediately after reperfusion or stopped within 1 day following transplantation were excluded (>95% of all grafts functioned at 1 day following transplantation). The native heart of the recipient was not surgically manipulated and remained functional.

DNA microarrays

dscDNA was synthesized from RNA samples by means of the SuperScript Choice system (Life Technologies, Rockville, MD) and a T7-(dT) 24 Primer (Genset Oligos, La Jolla, CA). The cDNA was purified using phenol/chloroform extraction with Phase Lock gel (Brinkmann Instruments, Westbury, NY) and concentrated by ethanol precipitation. In vitro transcription was performed to produce biotin-labeled cRNA using a BioArray HighYield RNA Transcript Labeling kit (Enzo Diagnostics, Farmingdale, NY) according to the manufacturer’s instructions. cRNA was linearly amplified ~40-fold with T7 polymerase using dscDNA that was synthesized. The biotinylated RNA was purified with an RNeasy Mini kit (Qiagen, Valencia, CA), fragmented to 50–200 nt, and then hybridized to an Affymetrix murine array (Mu11kB; Santa Clara, CA), which contains probe sets for 6500 genes and expressed sequence tags. After being washed, the array was stained with streptavidin-PE (Molecular Probes, Eugene, OR), amplified by biotinylated anti-streptavidin (Vector Laboratories, Burlingame, CA), and analyzed on a Hewlett-Packard Genearray scanner (Cupertino, CA).

Statistics and data analysis

Array data was analyzed with Microarray Suite 4.0.1 (Affymetrix). A single expression level for each gene was derived from the 20 probe pairs representing each gene, 20 perfectly matched (PM) and mismatched (MM) control probes. The MM probes act as specificity control that allow the direct subtraction of background and cross-hybridization signals. Each array was normalized to a standard of 2500 U/probe set. To determine the quantitative RNA level, the average of the differences (avg diff) representing PM-MM for each gene-specific probe set was calculated. The expression of each probe set was categorized as present (P), marginal (M), or absent (A). Calculations of means and variances were performed with JMP statistical software (SAS Institute, Cary, NC).

Real-time quantitative PCR

Total RNA isolated from murine untransplanted and transplanted allogeneic and syngenic graft heart samples was reverse transcribed using SuperScript II RNase Reverse Transcriptase (Life Technologies, Carlsbad, CA). Specific primer pairs were designed using the Primer Express software (Applied Biosystems, Foster City, CA). Direct detection of the PCR product was monitored by measuring the increase in fluorescence caused by the binding of SYBR Green to dsDNA. Reactions were performed in a MicroAmp Optical 96-well reaction plate (Applied Biosystems) using, for each separate well, 5 µl of cDNA mix, 5 µl of primer, and 10 µl of SYBR Green Master Mix (Applied Biosystems). Each well contained the primer pair for amplification of one of the parameters of interest. The gene-specific PCR products are continuously measured by means of the GeneAmp 5700 Sequence Detection system (Applied Biosystems) during 40 cycles. All experiments were run in duplicate and the same thermal cycling parameters were used. Nontemplate controls and dissociation curves were used to detect primer-dimer conformation and nonspecific amplification. The threshold cycle (CT) of each target product is determined and set in relation to the amplification plot of GAPDH. The CT is the number of PCR cycles required for the fluorescence signal to exceed the detection threshold value. The detection threshold is set to the log linear range of the amplification curve and kept constant (0.05) for all data analysis. With the PCR efficiency of 100%, the difference in CT values ({Delta}CT) of two genes can be used to calculate the fold difference (fold difference = 2 -(CT1-CTcontrol) = 2-{Delta}CT). The relative quantitation results are used to determine patterns of graft gene expression change in response to allogeneic and syngeneic transplantation (19, 20).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
To analyze gene expression during an in vivo alloimmune response, we performed murine heterotopic cardiac transplants in an allogeneic (BALB/c->B6) strain combination that has a complete MHC class I and II mismatch. As previously reported, these grafts are rejected at ~8 days (not shown). RNA was prepared from untransplantated control donor strain hearts, untransplanted recipient strain lymph nodes, and grafts at day 1 and 7 following transplantation. RNA was then analyzed with oligonucleotide microarrays. Total fluorescence for each array was normalized, absolute difference values were calculated, and each gene was determined to be A, M, or P by Microarray Suite algorithms. All genes that lacked a present call in at least one sample were masked from subsequent analyses. To determine the reproducibility of array data, we analyzed duplicate samples from control RNA from untransplanted hearts. Scatter plots showed a strong linear correlation (r = 0.98) of positively expressed genes (Fig. 1Goa). Analysis of the distribution of the ratios of control values showed a variance of only 0.09. As expected, a scatter plot of lymph node vs control heart showed increased dispersion (r = 0.29) with a variance of 18.61 (Fig. 1Gob), due to differential gene expression in the heart and lymph node tissues. To determine the modulation of gene expression following transplantation, we analyzed the ratios of the days 1 and 7 graft heart vs untransplanted control heart RNA. The scatter plot of day 1 vs control showed increased dispersion (r = 0.87), which was confirmed by analysis of the distribution of the ratios that had an increased variance of 1.49 (Fig. 1Goc). The scatter plot of day 7 vs control showed further increased dispersion (r = 0.60), which correlated with a greater variance of 13.83 (Fig. 1God). These results demonstrate an increase in differentially expressed genes during the rejection process.



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  		FIGURE 1. Scatterplot of expression profiles. RNA was harvested from duplicate control hearts, lymph nodes, and allogeneic grafts from days 1 and 7, and was hybridized to the Mu11kB DNA microarray; the average difference values were calculated for each probe set by Microarray Suite software. For each plot, the ordinate is control heart (sample 1). The abscissas are control heart (sample 2; a), lymph node (b), day 1 allograft (c), and day 7 allograft (d). An absolute call was determined for each probe set as P (black), M (black), or A (gray). Correlation coefficients were calculated using values that were determined to be present in at least one sample.

 
Of a total of 6519 genes analyzed by the microarrays, 974 were present in control hearts (Fig. 2Go). Of these 974 genes expressed at baseline, only 593 were detected in grafts 1 day following transplantation; however, 288 genes that were not detected in the control samples were present at day 1. At day 7 following transplantation, 495 of the genes that were expressed in the control sample were still detected, plus 97 genes, that were in the control but absent at day 1, were re-expressed. Also, 122 of the new genes expressed at day 1 were still detected at day 7, plus 212 additional genes not previously detected were present. Thus, almost half (479 of 974) of the genes constitutively expressed in control hearts were lost to detection during the rejection process, whereas a total of 400 new genes were detected due to the response to injury and the induction of inflammation.



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  		FIGURE 2. Flow diagram of expressed genes that were present following transplantation. A total of 6519 genes were analyzed in control hearts days 1 and 7 following transplantation and were categorized as P or absent/marginal (A).

 
To obtain an overview of the kinetics of allograft rejection, we analyzed absolute difference gene expression at days 1 and 7 compared with control samples by hierarchical clustering algorithms which perform pairwise calculations for each gene using Pearson correlation coefficients (Fig. 3Goa). As expected, the two control samples showed minimal dissimilarity. The days 1 and 7 groups were moderately dissimilar, but these posttransplant groups were most dissimilar from the untransplanted control samples. Next, we analyzed the genes most highly up-regulated at day 1 following transplantation compared with control by two approaches, fold change and absolute change in expression (Table IGo). Fold change analysis is less sensitive for detection of genes expressed constitutively, whereas absolute change is less sensitive for detection of genes expressed at low levels; therefore, the two approaches can detect different subsets of differentially regulated genes. First, using fold increase in expression, we detected metallothionein, calpactin I H chain, major excreted protein, and T lymphoma oncogene. Second, using absolute increase in expression we detected IFN {beta}, serglycin, EN-7, osteopontin, Mac-2, Eta-1, {beta} tubulin, fibronectin, gly96, mbh1. Of note, data analysis by these two approaches detected different subsets of genes; however, both approaches identified genes known to be involved in response to injury or stress. Importantly, at day 1 there was a conspicuous lack of immune genes with known associations with lymphocytes supporting the hypothesis that the early phase is predominantly lymphocyte independent. However, at day 7 following transplantation, numerous immune related genes were detected (Table IGo). Based on fold increase, highly up-regulated genes included invariant chain, MHC class II H chain, C1q C-chain, C1q {alpha}-chain, {beta}2-microglobulin, class I H-2Dk, and C1q B-chain. In addition, based on an absolute increase in expression, we detected Mac-2 and TCR {beta}-chain. Taken together, these results suggest that the early response at day 1 following transplantation consists predominantly of the innate immune response to injury and stress, whereas the later response at day 7 includes many components of adaptive immunity.



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  		FIGURE 3. Dendrogram of gene expression following transplantation. Agglomerative hierarchical clustering algorithms were used. x-axis distance is proportional to the dissimilarity between groups. a, Dendrogram of control hearts and days 1 and 7 graft hearts. b, Dendrogram of control hearts, control lymph nodes, and days 1 and 7 graft hearts. c, Dendrogram of allogeneic graft, alymphoid graft, allogeneic native, and control hearts.

 

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  		Table I. Genes up-regulated following transplantation1

 
Microarray data was independently verified by quantitative real-time PCR. Duplicate allogeneic and syngeneic murine cardiac transplants were performed. Primers were created from genes identified in Table IGo as up-regulated by fold increase and by absolute increase (Mac-2, serglycin, 24p3, C1q subunits, and {beta}2-microglobulin). Overall, the expression patterns for allogeneic graft samples as measured by these two independent methods were similar (Fig. 4Go and Table IGo). In both the allogeneic graft microarray and PCR data, 24p3 is highly expressed at day 1 but not in day 7,serglycin and Mac-2 are highly expressed in both allogeneic days 1 and 7 and C1q subunits and {beta}2-microglobulin are highly expressed at day 7.



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  		FIGURE 4. Independent verification of microarray quantitation. Relative mRNA levels of 24p3, Mac-2, C1q subunits, serglycin, and {beta}2-microglobulin (b2-M) were measured with quantitative real-time PCR in repeat murine allogeneic and syngeneic cardiac transplants. Each data point was normalized to mRNA expression of the untransplanted control heart. Normalization of the data to control hearts allows for comparison of the PCR results to the microarray fold change and absolute difference data. In the allogeneic transplants, quantitation with both methods gave similar patterns of gene expression.

 
To differentiate between injury-induced genes and those expressed in response to an immune response, repeat syngeneic transplants were performed and the graft mRNA analyzed by real-time PCR (Fig. 4Go). Consistent with previously described gene functions, the syngeneic day 1 data suggest that 24p3, Mac-2, and serglycin are genes likely expressed in response to the injury associated with transplantation. 24p3 is a known acute phase protein and Mac-2 is a galactose binding lectin and a monocyte/macrophage differentiation and activation marker. (21) Serglycin, the major proteoglycan of endothelial cells, (22) is up-regulated in response to inflammatory mediators TNF-{alpha} and IL-1{alpha} (23). {beta}2-microglobulin, present on all nucleated cells as a subunit of the class I MHC, is up-regulated on syngeneic day 1 only. Consistent with our observations, {beta}2-microglobulin is up-regulated by inflammatory mediators IFN{alpha}, IFN{beta}, IFN-{gamma}, and TNF-{alpha} (24) which, in the syngeneic graft, are likely generated due to the tissue injury of transplantation. In the allogeneic graft, {beta}2-microglobulin is further up-regulated at day 7 due to rejection. The C1q subunits are not up-regulated in the syngeneic PCR data suggesting that C1q is not involved in the injury or stress response to transplantation.

To test the kinetics of rejection in this model, we repeated the hierarchical clustering algorithm with inclusion of our control lymph node sample (Fig. 3Gob). These results clearly show association between the day 7 allograft and lymph node samples, whereas the day 1 sample associates more closely with the untransplanted control heart samples. To confirm that our data differentiated heart and lymph node genes, we analyzed expression ratios identifying invariant chain, MHC class II, Ig{kappa} chain (two different fragments) as the most highly expressed relative to heart (Table IGo). Conversely, genes most highly expressed in hearts compared with lymph nodes included myoglobin, adenine nucleotide translocase-1, and cytochrome c (Table IGo). These results are consistent with our prior information of gene expression in lymph node and heart tissues.

To evaluate the overall contribution of innate immunity to the early response, we determined the dissimilarity among transcriptional profiles of samples from alymphoid (BALB-recombinase-activating gene (RAG)->B6-RAG) graft hearts, allogeneic (BALB/c->B6) graft hearts, native hearts, and untransplanted control hearts using a hierarchical clustering algorithm (Fig. 3Goc). The alymphoid group lacks functional T and B lymphocytes due to the deficiency of RAG. Our results show that at day 1 following transplantation, the alymphoid and allogeneic grafts have only a small amount of dissimilarity indicating that the early response is, at least in part, independent of adaptive immunity. At day 7 following transplantation, the profile of the alymphoid group is similar to control hearts suggesting that in the absence of an alloimmune stimulus, the inflammatory response resolves. In contrast, the allogeneic group at day 7 following transplantation is highly dissimilar from all other experimental groups consistent with the generation of an adaptive response.

Based on our results showing modulated expression of substantial numbers of genes 1 day following transplantation, we next analyzed expression 6 h following allogeneic transplantation. To detect clusters of genes with similar patterns of expression in the various experimental groups, we applied SOM to microarray data from samples from allogeneic grafts at 6 h, 1 day, and 7 days; in addition, we included control data from alymphoid grafts and untransplanted control hearts (Fig. 5Go and Table IIGo). Using a heuristic approach to optimize clustering and minimize SD, we tested numerous geometries, numbers of epochs, and normalization parameters. Using a 3 x 4 geometry, 100 epochs, and a row variation threshold of 3, we generated 12 clusters (0–11) that differentiated gene expression in terms of the kinetics of rejection. A comparison of clusters 2 and 8 support the specificity of the analysis based on comparison of clustered genes with prior biological information. Cluster 2 contains a cluster of 37 genes with low expression in lymph node, but high expression in all heart samples. Genes included in cluster 2 include myosin H chain, myosin L chain 2, cytochrome c oxidase, myoglobin, myosin L chain, troponin I, {alpha} cardiac actin (Table IIGo). In contrast, cluster 8, which has high expression in lymph node, includes CD3{delta}, T11, mb-1, Ig L chain, H2-M, p56-tck, Thy-1, and TCR{beta} among others. These results are consistent with previous studies of heart and lymph node tissue.



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  		FIGURE 5. SOM. Clusters of genes with similar patterns of expression in the experimental groups were identified using SOM. Experimental groups distributed on the x-axis include control heart 1 ({diamondsuit}), allogeneic graft 6 h following transplantation (), allogeneic graft 1 day following transplantation ({triangleup}), allogeneic graft 7 day following transplantation (*), alymphoid graft (), and control lymph node ( ). The y-axis is relative expression, which is autoscaled to enhance visualization and is thus variable among the 12 profiles. The mean expression is depicted as the symbols with error bars indicating 2 SD. The number of genes in each cluster is 23 (c0), 18 (c1), 37 (c2), 50 (c3), 26 (c4), 20 (c5), 30 (c6), 47 (c7), 46 (c8), 22 (c9), 24 (c10), and 29 (c11). The algorithm was initiated with 3 x 4 geometry using 100 epochs. Based on multiple heuristic observations, increased numbers of nodes produced clusters with low numbers of genes, whereas decreased numbers of nodes produced larger SD. Increasing the number of epochs (=500) did not produce detectable changes in the clusters or SD.

 

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  		Table II. Gene clusters selected by SOM1

 
Additional clusters differentiated genes expressed during the early and late phases of rejection. Cluster 0 includes a cluster of 23 genes highly up-regulated at day 7 following transplantation, but not highly expressed in lymph nodes. This cluster includes several complement genes, acrogranin, Mac-2, and IFN-{gamma}-inducible protein (IP)-30 precursor. Cluster 4 includes 26 genes also up-regulated at day 7, but in addition, these genes are highly expressed in lymph nodes. Cluster 4 includes TCR, I-A{beta}, I-E{beta}, I-A{alpha}, P-selectin, 20S proteasome, CD53, and calmodulin. These results differentiate genes highly expressed at day 7 based on high or low expression in lymph nodes and delineate two components of the late phase of rejection. In contrast, cluster 11 identified a cluster of 29 genes that are highly up-regulated at 6 h following transplantation. Genes up-regulated at this early time point include IL-1R, macrophage-inflammatory protein (MIP)-2, IL-6, haptoglobin, MIP-1{beta}, I{kappa}B{alpha}, CD14, and metalloproteinase-3 tissue inhibitor, among others. Consistent with the expression profile, these genes have been associated with the acute phase response and early proinflammatory responses, but in general are not major components of adaptive immunity.

Several clusters, including 5, 6, 9, and 10, include genes up-regulated in both the allogeneic and alymphoid groups. Genes in these clusters include many genes that have been associated with stress, injury, and wounding, but conspicuously lack genes commonly associated with adaptive immunity. For example, genes included in these clusters include cofilin, profilin, ferritin, calmodulin, secreted protein acidic-rich cysteine, Gla, major excreted protein, GPI, vimentin, heat shock protein (HSP)60, integrin {beta} subunit, junD, serum amyloid A 3, IFN{beta}, serglycin, calpactin I HSP70, and lactate dehydrogenase A among others. Interestingly, as shown in cluster 3, there is also a subset of genes down-regulated following transplantation in both the allogeneic and alymphoid recipients suggesting that these genes are down-regulated in response to stress or injury. Taken together, the SOMs have differentiated clusters of genes with distinct patterns of expression that identify distinct phases of the process of alloimmunity.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Most studies of transplantation have analyzed a small subset of parameters, often a single gene product, in attempts to understand the pathogenesis of rejection. For example, in murine heterotopic heart transplantation, a large number of studies analyzing knockout strains, which were selected based on prior data suggesting they would be important in the alloimmune response, have been studied. Interestingly, many of the knockout strains studied, including strains deficient in cytokines, chemokines, receptors, adhesion molecules and transcription factors, produce a similar outcome: rejection is delayed, but not indefinitely prevented. These observations suggest that the alloimmune response includes multiple components that are functionally overlapping and capable of compensating, at least partially, for gene deficiencies. Based on these arguments, we reasoned that a global analysis of gene expression during allograft rejection using oligonucleotide microarrays could provide important insights into the alloimmune response.

A previous study of cardiac allograft rejection using DNA microarrays reported that the majority of highly inducible genes, which were analyzed at day 5 following transplantation, were IFN-inducible (25). We previously reported that serum levels of IFN-{gamma} surged at day 5, but had already decreased by day 7 (13). Increased levels of serum IFN-{gamma} at day 5 are consistent with the identification of IFN-{gamma}-inducible genes in the graft; however, decreasing levels of serum IFN-{gamma} at day 7, which corresponds to the time of graft rejection, suggest that IFN-{gamma} is not a crucial component of the rejection process. This interpretation is confirmed by the development of acute rejection in IFN-{gamma} deficient allograft recipients (26). Thus, multiple IFN-{gamma}-independent components of alloimmunity, which remain poorly characterized, are necessary to promote rejection.

In our study, the SOM identified clusters of up-regulated genes as early as 6 h after transplantation. SOM analysis differentiated clusters of genes up-regulated 6 h following transplantation based on different levels of expression during late phases of rejection and in lymph node tissue. For example, cluster 11 contains 29 genes highly up-regulated at 6 h, but not up-regulated in any of the other experimental groups. Cluster 11 includes IL-1R, IL-6, and haptoglobin, which have all been associated with the acute phase response (27, 28, 29, 30). In addition, cluster 11 includes MIP-2, a chemokine known to be induced by ischemia, MIP-1{beta}, an early component of the inflammatory response, and CD14, an important component in Toll-like receptor 4 signaling during innate immune responses (31, 32, 33). Also included was the immediate early gene 3CH134, which has been shown to be up-regulated by ischemia (34, 35). Up-regulation of 3CH134 at the 6-h time point is consistent with its known expression as an immediate early factor and inducibility by ischemia. Of note, two independent chip probes hybridized to the I{kappa}B{alpha} gene, which has been previously shown to be up-regulated following transplantation (36) supporting the reproducibility of the analysis.

The SOM also identified genes up-regulated at both 6 h and 7 days following transplantation. For example, cluster 4 contains 26 genes up-regulated at both early and late times following transplantation (and in lymph node tissue). Genes in this cluster include MHC class II (identified by four different probes) and TCR, genes necessary for rejection (7, 9), P-selectin, a gene important in cell trafficking and rejection (37), CD53, a pan-leukocyte marker that has been shown to activate lymphocytes (38), and Lmp7, a component of the 20S proteasome important for Ag processing (39). The mean level of expression of these genes was greater at day 7 than at 6 h, which is consistent with the association of these genes with Ag-specific responses.

The third important pattern of expression includes genes up-regulated at day 7 following transplantation, but not in any of the other groups. Because allograft rejection occurs at ~7.8 days in our experimental protocol, these genes are highly up-regulated immediately before the time of rejection. Genes included in cluster 0 are IP-30 (40), Mac-2, a macrophage cell surface protein that binds extracellular matrix (41, 42), monopolar spindle 1, AP50, a component of the CTLA-4 signaling complex (43, 44), and C1q (identified by four different probes), which is a C-type lectin member of the collectin family important in innate immunity and up-regulated in the serum following renal transplantation (45, 46). Our results suggest that complement may be important in allogeneic rejections.

Clusters 1, 5, and 10 identified genes that were up-regulated in all groups, including alymphoid grafts, following transplantation suggesting induction by Ag nonspecific mechanisms such as ischemia, injury, or stress. Consistent with this notion, cluster 1 includes fibronectin (47, 48); HSP47, a collagen-binding chaperone induced by stress and transformation (49, 50); SPARC, a matricellular protein that responds to injury (51); collagen components (identified by four different probes) important in wound injury (52); the serine protease cathepsin D (53); and L14, a S-type lectin (54). Clusters 5 and 10, which include calmodulin, shown to be induced by ischemia (55); serum amyloid A 3, a component of the acute phase response (56); IFN{beta}, a factor that accelerates rejection (57); serglycin, induced by TNF-{alpha} and IL-1{alpha} (23); calpactin I sulfated glycoprotein (Sgp) 1, previously identified in Sertoli cells and preimplantation embryos, but without a clear association with transplantation (58, 59); and HSP70, a gene up-regulated following transplantation and shown to be protective against ischemic injury (60, 61), are consistent with the concept that these genes function in Ag-independent responses. Interestingly, we also identified genes (cluster 3) down-regulated in all groups following transplantation. Of note, both cyclophilin and FK506 binding protein were up-regulated posttransplantation.

Cluster 8 identified 46 genes that are highly expressed in lymph nodes, but not hearts, and cluster 2 detected 37 genes highly expressed in hearts, but not lymph nodes. Neither of these clusters showed differential expression following transplantation; however, the specific genes identified are consistent with previous biological characterizations of heart and lymph node tissues. Genes highly expressed in hearts (cluster 2) include B2-crystallin, myosin H chain, myosin L chain 2, cytochrome c oxidase, myoglobin, ventricular alkali myosin L chain, troponin I, {alpha}-cardiac actin, G3PDH (two probes), cardiac {alpha}-actin, cytochrome c oxidase VIIc, and NADH dehydrogenase. In contrast, genes highly expressed in lymph nodes (cluster 8) include CD3{delta}, T11, mb-1, Ig L chain, H2-M, p56-tck, Thy-1, TCR{beta}, IgH (two probes), T cell-specific transcription factor, and Ig{kappa} (three probes). These observations strongly support the power of SOM to cluster genes according to biological functions.

Microarray studies are, in general, limited by the probes present on the array. The arrays used in our study consist of probes for only a portion of the mouse genome. Specific genes that may prove to be important in graft rejection may be absent from the array used in this study. Our results then are a representation of the genes involved in transplantation.

Our results demonstrate the dynamic and complex character of alloimmunity. Our kinetic analysis using hierarchical clustering dendrograms differentiated the rejection process into two broad phases: the early (innate) and late (adaptive) phases. The identification of specific genes in each phase indicates that the innate and adaptive phases are composed of multiple components. For example, based on correlations with prior understanding of gene function, our analysis demonstrates that the innate phase includes complement, stress, ischemia, and injury components. Similarly, the adaptive phase includes markers for CD4, CD8, and B cell responses. And it is not unreasonable that additional components are operative during rejection. Taken together, these observations pose an important question: what are the minimal components necessary to promote rejection? Identification of these components would suggest definitive diagnostic criteria of rejection and essential targets for therapeutic intervention. Traditional reductionist approaches have attempted to identify (with limited success) a single or small number of genes essential for the rejection process. Our results suggest the possibility that the in vivo alloimmune response includes multiple components that are regulated in a modular system or network. A profound understanding of the complex biological response of alloimmunity will likely require the integration of reductionist studies and global analyses.


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  		Table 2A. Continued

 

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  		Table 2B. Continued

 

   Acknowledgments
 
We thank Jim Lederer for the control lymph node data and Walter Zybko and Min Xu for technical support.


   Footnotes
 
1 This work was supported by an American Heart Association Established Investigator Award, an Arthritis Foundation Research Award, and National Institutes of Health Grant RO1 AI44085 (to D.L.P.). Back

2 K.C. and T.F.M. contributed equally to this work as first authors. Back

3 Address correspondence and reprint requests to Dr. David L. Perkins, Brigham and Women’s Hospital, PBB 170, 75 Francis Street, Boston, MA 02115. E-mail address: dperkins{at}rics.bwh.harvard.edu Back

4 Abbreviations used in this paper: SOM, self-organizing map; PM, perfectly matched; MM, mismatched; P, present; M, marginal; A, active; CT, cycle threshold; MIP, macrophage-inflammatory protein; RAG, recombinase-activating gene; IP, IFN-{gamma}-inducible protein; HSP, heat shock protein; Sgp, sulfated glycoprotein. Back

Received for publication October 16, 2001. Accepted for publication March 26, 2002.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
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