Journal of Leukocyte Biology Myeloid cells, immune suppression, tumor immunology
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(Journal of Leukocyte Biology. 2002;71:348-358.)
© 2002 by Society for Leukocyte Biology

Molecular profiles of allograft rejection following inhibition of CD40 ligand costimulation differentiated by cluster analysis

Scott M. Damrauer*, Rachel DeFina*, Hongzhen He{dagger}, Kathleen J. Haley{dagger} and David L. Perkins*

* Laboratory of Molecular Immunology,
{ddagger} Pulmonary Division,
{dagger} Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts

Correspondence: David L. Perkins, Brigham & Women’s Hospital, PBB-170, 75 Francis St., Boston, MA 02115. E-mail: dperkins{at}rics.bwh.harvard.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Recent technological advances in biomedical research, such as genome sequences and DNA microarrays, have dramatically increased the size of relevant databases. A major challenge is the extraction of a limited number of parameters from these databases that can differentiate and diagnose complex biological states. In a model of cardiac transplantation investigating immunosuppression by inhibition of CD40 ligand costimulation, we have applied a combination of cluster algorithms and self-organizing maps to analyze a panel of 60 candidate genes. Dendrograms generated by cluster analysis distinguished different molecular bases of rejection. Using self-organizing maps, we identified nine genes (CD4, CCR3, CCR5, LTß, MIP-1{alpha}, MIP-2, CD8{alpha}, IP-10, and RANTES), each with a unique profile of transcriptional expression, that reproduce the differentiation of states of rejection in dendrograms. Using histology and immunohistochemistry, we correlated differential regulation of CD4 and CD8 at the levels of mRNA and protein. Our strategy of data reduction successfully decreased the number of genes to nine, which are sufficient to differentiate distinct states of rejection in our experimental protocol.

Key Words: chemokines • cytokines • transplantation • self-organizing maps


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A major challenge in immunobiology involves the interpretation of global responses in whole organisms [1 , 2 ]. To address complex biological problems such as allograft rejection in terms of global responses, it is necessary to assess and interpret large databases. However, the optimal size of databases necessary to interpret in vivo biological responses has not been established. With the advent of gene-chip technology, it is feasible to accumulate expression profiles of thousands of genes in a single experiment. It is interesting that several recent studies quantitating the expression of large numbers of genes using microarray technology have shown that a small subset of these genes is sufficient to differentiate melanomas [3 ], breast cancers [4 ], and rhabdomyosarcomas [5 ]. Thus, information of a small subset of the transcriptome may be sufficient to differentiate some complex biological responses.

The advantage of the gene-chip approach is the possibility of serendipitous discoveries and the numerous data points accumulated; however, many of these data points involve genes that have expression profiles that are negative, unchanged, or not relevant to the biological question. An alternative strategy to profiling the complete transcriptome is to analyze groups of candidate genes selected on the basis of prior knowledge of relevant function. The advantages of focusing on a group of candidate genes are the decreased amount of data analysis and lower cost. A recent study that analyzed the expression of more than 5000 genes in breast tumors with microarrays differentiated BRCA1+, BRCA2+, and nonfamilial tumors based on a subset of only 51 genes [4 ]. In the current study, we investigated a panel of 60 candidate genes that includes inflammatory molecules, cytokines, chemokines, chemokine receptors, and cell-surface markers that previously have been shown to be important mediators of inflammation and immunity.

To differentiate allograft rejection in terms of kinetic development and response to a treatment protocol that blocks CD40 ligand (CD40L) costimulation, we used two types of algorithms. First, the dissimilarity of each rejection profile was compared by a clustering algorithm and was visualized in hierarchical dendrograms. Second, we grouped genes with similar patterns of expression by self-organizing maps (SOM) into subsets that were regulated coordinately. Cluster is an agglomerative hierarchical algorithm that calculates the dissimilarity among experimental groups based on summation of Pearson correlation coeffecients, and it has been applied to the analysis of gene expression profiles detected with microarrays [6 ]. SOM are generated by an iterative nonparametric algorithm, which calculates euclidean distance, and has been applied to engineering problems, interpretation of electroencephalograms, economic data, as well as to gene-expression profiles [7 , 8 ]. In our study, the combination of these algorithms defined a molecular basis for allograft rejection that differentiated multiple experimental groups including different time points and treatment protocols.

Our experiments examined the global effects of inhibition of an important pathway in allograft rejection, the CD40/CD40L-costimulatory pathway. As previously shown, allograft survival in murine and primate models is prolonged markedly by CD40L blockade [9 10 11 ]. We integrated the profiles of expression of the 60 candidate genes during allograft rejection by cluster analysis into a single dendrogram that differentiated a molecular basis for the delayed rejection following CD40L blockade compared with untreated acute rejection. In addition, cluster analysis clearly distinguished the kinetic stages of the rejection process into an early and late phase, which correlated with the expression of different subsets of genes. Further, confirming the robustness of the SOM algorithm for data analysis, we demonstrate that the selection of only nine genes, one from each subset defined by SOM, is sufficient to recapitulate the differentiation of the experimental groups in dendrograms produced by cluster analysis. Thus, our strategy of data reduction successfully decreased the number of genes, which are necessary to differentiate distinct states of rejection in our experimental protocol, to nine.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Vascularized heterotopic cardiac transplantation
Murine hearts were transplanted as previously described [12 ]. Briefly, hearts were harvested from freshly sacrificed donors and were transplanted immediately into 8- to 12-wk-old recipients, which were anaesthetized via intraperitoneal injection with 60 mg/kg 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 less than 60 min from the time the donor heart was harvested. Donor hearts that did not beat immediately after reperfusion or stopped within two days after transplantation were excluded (>95% of all grafts functioned at 2 days after transplantation). Donor grafts were harvested at the indicated times after transplantation for preparation of RNA.

Mice
Eight- to 12-wk-old male mice, including BALB/cByJ (BALB/c; H-2d), C57BL/6J (B6; H-2b), C57BL/6J-Rag1tm1Mom (B6-Rag KO; H-2b; JAX, Bar Harbor, ME), and BALB/c-AnNTac-Rag2tm1N12 (BALB/c-Rag KO; H-2d; Taconic, Germantown, NY), were used as donor and recipients in the transplant experiments. Experimental groups include syngeneic (B6->B6), alymphoid (B6-RAG->BALB/c-RAG), and allogeneic (B6->BALB/c). In the studies of CD40L blockade, allogeneic recipients were treated with 250 µg monoclonal antibody (mAb) MR1 [13 ] on days 0 and 1 after transplantation. Mice are maintained in vented racks with constant temperature and humidity in our animal facility under virus antibody-free conditions.

Ribonuclease protection assay (RPA)
Chemokine, chemokine receptor, and CD marker expression were analyzed by RPA. Briefly, total RNA was isolated from hearts using RNAzol and analyzed using the RiboQuant Multi-Probe RPA system (Pharmingen, San Diego, CA). RNA (15 µg) was used per hybridization and RNase reaction with the templates mCK1 [interleukin (IL)-4, IL-5, IL-10, IL-13, IL-15, IL-9, IL-2, IL-3, and interferon-{gamma} (IFN-{gamma})], mCK2 [IL-12p35, IL-12p40, IL-10, IL-1{alpha}, IL-1ß, IL-1RA, IL-18, IL-6, IFN-{gamma}, and migration inhibitory factor (MIF)], mCK3 [LT{alpha}, LTß, tumor necrosis factor {alpha} (TNF-{alpha}), IL-6, IFN-{gamma}, IFN-ß, transforming growth factor (TGF)-ß1, TGF-ß2, TGF-ß3, and MIF], mCK-5 [MIF, RANTES (regulated on activation, normal T expressed and secreted), eotaxin, macrophage-inflammatory protein (MIP)-1ß, MIP-1{alpha}, MIP-2, IP-10, and monocyte chemoattractant protein-1 (MCP-1)], mCR-5 (CCR1, 1ß, 4, 5, and 2), a custom template [inducible nitric oxide synthase (iNOS), CXCR2, 3, 4, 5, CCR6, 8{alpha}, and 8ß], and mCD-1 [T-cell receptor (TCR)-{delta}, TCR-{alpha}, CD3{varepsilon}, CD4, CD8{alpha}, CD8ß, CD19, F4/80, and CD45; Pharmingen, San Diego, CA]. The IP-10 template detects the C57BL/6 allele [14 ]. The protocol was modified by labeling probes with 35S. After hybridization with the 35S-labeled probes, the samples were treated with RNase and purified according to the manufacturer’s protocol. The protected probes were electrophoresed on a denaturing 5% polyacrylamide gel. The gels were exposed in a Molecular Dynamics phosphorimager. The identity of each protected fragment was established by analyzing its migration distance against a standard curve of the migration distance versus the log nucleotide length for each undigested probe. Samples were normalized to the housekeeping gene, glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Protected bands were quantitated by densitometry analysis using ImageQuant software (Molecular Dynamics, Sunnyvale, CA). From 18 experimental groups, a total of 1080 values were calculated as means from 1708 RPA results and were subjected to cluster analysis.

Immunohistochemistry
Graft hearts from transplant recipient or control mice were embedded in ornithine carbamyltransferase compound, snap-frozen in liquid nitrogen, and cut into 4-µ sections. After mounting onto glass slides, tissue was fixed in acetone for 10 min and blocked with normal mouse serum diluted 1:10. Primary antibodies were rat anti-CD8 (20 µg/mL; Pharmingen, LaJolla, CA ), anti-CD4 (20 µg/mL; Pharmingen, LaJolla, CA ), and anti-F4/80 (5 µg/mL; Serotec, Raleigh, NC). Slides were incubated in the primary antibody overnight at 4°C, followed by a biotinylated mouse anti-rat secondary antibody (1:500; Jackson, West Grove, PA) for 2 h at 4°C, a methanol and hydrogen peroxide wash (10:1), and a 30-min incubation in streptavidin horseradish peroxidase (1:1000; Jackson). The tissue was stained with 3'-diaminobenzidine tetrahydrochloride (DAB) substrate and counterstained with methyl green (Sigma Chemical Co., St. Louis, MO). Images were captured using a Leica DMLB microscope interfaced with Leica Q500IW image analysis software.

Statistics
Graft survival data were calculated as mean ± SD. Survival data were based on six to eight transplants per group. Comparisons among groups were performed using a nonparametric Mann-Whitney test, and differences were considered significant at P < 0.05. Differential expression of mRNA determined by RPA on days 0, 1, 3, 5, and 7 in the syngeneic, allogeneic, and MR1-treated recipients and at the time of rejection (days 32–33) in the MR1-treated group was analyzed by two-factor analysis of variance (ANOVA) with the variables gene expression and experimental group. Statistical significance of variances was calculated for P < 0.05 by F-test.

Cluster analysis
Cluster analysis was performed using Cluster and TreeView [6 ] (courtesy of M. Eisen, Lawrence Livermore Radiation Laboratory, Berkeley, CA) and GeneCluster (courtesy of Whitehead Institute for Biomedical Institute, Cambridge, MA) software. RPA values were calculated as percent of GAPDH and analyzed by Cluster without normalization using the hierarchical clustering algorithm with complete linkage clustering. Briefly, dissimilarity is determined by calculation of the Pearson correlation coefficient among each series of values from each experimental group [6 ]. After processing, the dendrogram was visualized by TreeView. SOM were generated by GeneCluster without normalization using a 3 x 3 geometry of nine seed maps [8 ]. Nine maps were selected empirically to eliminate clusters with few genes or large standard deviations. The centroids and standard deviations of the groupings were analyzed using 50 epochs. Additional epochs did not alter the gene clusters of the maps.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Kinetic analysis of allograft rejection: distinct profiles of gene expression during the early and late phases of rejection
Our initial analysis focused on the kinetics of the in vivo response in the allogeneic (BALB/c->B6) recipients (Table 1 ). These grafts express a complete major histocompatibility complex (MHC) mismatch and generate a robust alloimmune response that results in rejection determined by the cessation of palpable heartbeats at approximately day 8 following transplantation. The complete panel of 60 candidate genes was analyzed by RPA at days 1, 3, 5, and 7 following transplantation. The results were quantitated by densitometry and analyzed by Cluster software (Fig. 1A ). Comparisons among experimental groups are based on an agglomerative algorithm that calculates the dissimilarity among each series of values of gene expression. These results clearly segregate the late (days 5 and 7) and early (days 1 and 3) profiles of the responses in the allogeneic group. As expected, the BALB/c and B6 untransplanted control samples from untransplanted hearts are nearly identical. In addition, the control samples are more similar to the early, than the late, allogeneic groups.


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  		Table 1. Experimental Groups (CD40L Blockade Was Produced)

 


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  		Figure 1. Dendrogram of kinetic analysis of rejection profile in transplant recipients. The level of expression of a panel of 60 candidate genes including inflammatory molecules, cytokines, chemokines, chemokine receptors, and cell-surface markers was determined at days 1, 3, 5, and 7 following transplantation by RPA of graft hearts and was analyzed by Cluster and Tree software [6 ] in allogeneic (a), syngeneic (b), and alymphoid (c) graft recipients, which are described in Table 1 . Controls are untransplanted BALB/c and B6 hearts. The degree of dissimilarity is proportional to the total length of the horizontal axis among groups.

 
Previous studies suggest that the biological response to transplantation involves multiple components including injury, inflammation, and immunity. To differentiate the adaptive allogeneic immune response from the nonadaptive or innate response as a result of ischemia/reperfusion, wounding, stress, and other forms of injury, we compared the allogeneic (BALB/c->B6) protocol with two control groups: a syngeneic (B6->B6) protocol that contains a complete array of functional lymphoid and inflammatory cells (however, because of the absence of alloantigen, no adaptive immune response should be stimulated) and an alymphoid (BALB/c-RAG KO->B6-RAG KO) protocol that lacks functional T and B lymphocytes because of deletion of the recombinase-activating gene (RAG) in the donor and recipient mice but contains all other lineages of inflammatory cells including natural killer (NK) cells (Table 1) . Kinetic analysis of the syngeneic groups shows that the day 1 group is most dissimilar from the other groups including days 3, 5, and 7, which show only a minor degree of difference (Fig. 1b) . Similarly, analysis of the alymphoid groups also shows that the day 1 group is most dissimilar (Fig. 1c) . Taken together, these results demonstrate that in all three experimental groups, the profile differs markedly during the early phase, in particular at day 1. In contrast, the day 7 profile differs only in the allogeneic protocol. Because the early day 1 changes occur in the syngeneic group in the absence of alloantigen and in the alymphoid group without functional lymphocytes, we conclude that the early response does not require an adaptive immune response.

Comparison of the innate and adaptive groups
To directly analyze the response contributed by the adaptive and nonadaptive immune responses, we compared the allogeneic and alymphoid groups by cluster analysis (Fig. 2A ). The resulting dendrogram shows the marked dissimilarity of the allogeneic day 7 group, with a lesser degree of dissimilarity between the allogeneic and alymphoid groups on day 1. As expected, the BALB/c and B6 control samples were nearly identical and only slightly different from the alymphoid day 7 group, suggesting that the injury and inflammation that occurred during the early phase in the alymphoid group had subsided by day 7. In the transplantation model, the recipient retains its unmanipulated, native heart in addition to the heterologous transplanted heart; thus, the native heart provides a control for systemic perioperative stress and nonspecific inflammatory responses. Our analysis shows minimal differences between control untransplanted and native hearts from the allogeneic-BALB/c->B6 group on day 1 or 7, indicating that our panel of genes was not up-regulated in response to systemic stress or nonspecific injury. Similar analyses did not detect differences in the native hearts from the syngeneic and alymphoid groups (unpublished results). Cluster analysis of the allogeneic and syngeneic groups showed similar results (syngeneic results not shown).



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  		Figure 2. Dendrogram of rejection profile in allograft recipients treated with a costimulatory blockade of CD40L. The level of expression of a panel of 60 candidate genes was determined by RPA and analyzed by Cluster and Tree software. (a) Includes graft hearts from allogeneic and alymphoid and native hearts from allogeneic recipients harvested at 1 and 7 days following transplantation; (b) includes recipients treated with MR1 mAb to block costimulation at days 0 and 1, harvested at days 1 and 7 and time of rejection (two representative recipients with graft rejection on days 33 and 32 are shown), and allogeneic and alymphoid recipients harvested at days 1 and 7. Controls are untransplanted BALB/c and B6 hearts. The degree of dissimilarity is proportional to the total length of the horizontal axis among groups.

 
Effect of CD40L blockade on the rejection profile
Short-term treatment of allogeneic graft recipients with an anti-CD40L mAb (MR1) prolongs median allograft survival to 32 days (Table 1) . To determine if our panel of candidate genes could differentiate between rejecting grafts in the MR1-treated recipients and untreated control groups, we generated a clustering dendrogram in the MR1-treated, allogeneic control, and alymphoid control groups (Fig. 2b) . In a comparison of the grafts at the time of rejection, the MR1-treated grafts (at day 32) could be differentiated distinctly from the untreated allogeneic group (at day 7). In addition, the treated and untreated-rejecting grafts were clearly differentiated from all of the other groups including the MR1-treated group at day 7 (the time when the untreated allogeneic group was rejecting). Not surprisingly, this observation indicates that the MR1-treated group at day 7 differs markedly from the untreated allogeneic group at the same time following transplantation. Also, all day 1 groups cluster together, suggesting that the early response, which presumably includes the injury and innate components, is not altered markedly by the MR1 treatment. The syngeneic groups map similar to the alymphoid recipients (syngeneic results not shown).

Coordinate expression of genes analyzed by dendrograms and SOM
In addition to our comparison of experimental groups, we also generated dendrograms of the candidate genes (Fig. 3A , left). A visual inspection of the color-coded expression levels shows subsets of genes with related expression patterns. For example, there is a subset with an increased pattern of expression in the MR1-treated grafts at day 32 at the time of rejection (Group A) and a second subset with increased expression in the untreated allogeneic group (Group B; Fig. 3a , right). These differential expression profiles form the basis for the dendrogram of the experimental groups (Fig. 3a , top). We also analyzed gene expression by ANOVA for two variables (Table 2 ). These results show decreased expression of multiple genes during MR1 treatment. For example, the chemokines MIP-1{alpha}, MIP-1ß, IP-10, and MCP-1 are all significantly decreased by MR1 treatment. ANOVA also detected marked changes in expression in the MR1-treated recipients at the time of rejection (day 32–33); for example, IFN-{gamma}, LTß, TNF-{alpha}, and MIP-2 were all significantly decreased. Analysis of the syngeneic and alymphoid recipients was also similar, because we did not detect significant changes in gene expression from native hearts of the syngeneic, alymphoid, or allogeneic recipients (not shown).



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  		Figure 3. Dendrogram of expression levels of candidate genes. The level of expression of a panel of 60 candidate genes was analyzed by RPA. (a) A dendrogram was generated by Cluster analysis comparing the expression profiles of individual genes (left). Group A (increased expression in two representative recipients treated with MR1 mAb with graft rejection on days 33 and 32) and Group B (increased expression at the time of rejection in the allogeneic group) are expanded with individual genes listed (right), and the corresponding dendrogram of the experimental groups was generated (top). Pseudocolor key of gene expression: blue (no increase); yellow (color intensity correlates with level of increased expression). (b) The same panel of genes was analyzed with GeneCluster [16 ] using SOM and separated into nine distinct maps, which are shown in boxes and numbered as Maps 0–8. In each box, the open circles show the mean level of gene expression, and the flanking lines indicate the standard deviations of the expression for each map. For each map, the circles from left to right represent expression for genes from BALB/c control (gray), B6 control (white), allogeneic day 1 (maroon), allogeneic day 7 (yellow), syngeneic day 1 (purple), syngeneic day 7 (green), alymphoid day 1 (red), alymphoid day 7 (black), MR1-treated day 1 (chartreuse), MR1-treated day 7 (blue), MR1-treated day 33 (orange), and MR1-treated day 32 (turquoise), respectively. The y-axis represents relative expression calculated for each SOM. Individual maps from 0 to 8 include a total of 23, 8, 7, 4, 2, 2, 7, 3, and 4 genes, respectively (see Table 3 ). The number of maps was determined empirically to minimize the standard deviations and maximize the number of genes per map.

 

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  		Table 2. Analysis of Variance of Gene Expression

 
Dendrograms produced by agglomerative clustering algorithms are powerful techniques with which to compare large amounts of data associated with an array of experimental groups and can provide insight into the relationships among groups. However, there are several limitations of agglomerative clustering. For example, the dendrogram assumes an inherent hierarchial organization that may not be present. Also, the degree of dissimilarity is expressed as a calculated sum of the total dissimilarity among experimental groups. However, the total dissimilarity may be produced by multiple, distinct biologic processes, which are summed into a single value obscuring various components of dissimilarity; thus, the effect of a biologic process may be difficult to evaluate in the summed dissimilarity value. Because each biologic process may correlate with genes that are coordinately regulated, we analyzed the level of gene expression in all of the experimental groups with SOM to determine the contribution of subsets of genes to the different profiles of rejection (Fig. 3b) [15 , 16 ]. These results show nine distinct patterns of gene expression, and the number of genes per map ranged from 2 to 23. Map 0 includes 23 genes that show small levels of change. Exclusion of all 23 genes of map 0 in a dendrogram analysis, such as in Figure 2 , does not change the relationship of the groups in the dendrogram, confirming the lack of differential expression of this subset of genes (unpublished results). In contrast, the level of gene expression at day 7 in the allogeneic group is increased in maps 3–8; however, the degree of increase and the level of expression in the other groups differ among maps. For example, map 8 includes four genes up-regulated at day 7 in the allogeneic group but not markedly changed in the other groups. Map 7 includes three genes that are highly up-regulated in the untreated allogeneic group at day 7 and are modestly up-regulated in the MR1-treated group at day 33. It is interesting that map 2 contains seven genes that are expressed most highly at the time of rejection (approximately day 33) in the MR1-treated recipients but are only up-regulated moderately in the other grafts.

Although the grafts in the MR1-treated and untreated groups are ultimately rejected (but with different kinetics), an analysis of the composition of the SOM suggests distinct mechanisms of rejection (Table 3 ). For example, genes that are highly expressed by T cells including TCR-{alpha}, CD3{varepsilon}, CD8{alpha}, and CD8ß are highly up-regulated in the untreated allogeneic group at day 7 but are only modestly up-regulated in the MR1-treated groups at day 7 or day 33 at the time of rejection (see Map 6). In contrast, genes highly up-regulated in the MR1-treated group at the time of rejection but only modestly up-regulated in the other groups include F4/80, CCR1, CCR2, CCR5, MCP-1, TGF-ß1, and TGF-ß3.


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  		Table 3. Clusters of Genes Selected by Self-organizing Maps

 
Maintenance of distinct dendrograms profiling rejection despite data reduction to a single member of each SOM
Cluster analysis based on a large number of genes demonstrates the power to differentiate different phases of rejection and different mechanisms of rejection produced by immunosuppressive treatment. However, clinical use of diagnostic criteria would be enhanced if lower numbers of genes would be adequate to differentiate the unique rejection responses. To evaluate the feasibility of using lower numbers of genes, we analyzed the extreme case of selecting a single gene from each SOM including CD4, CCR3, CCR5, LTß, MIP-1{alpha}, MIP-2, CD8{alpha}, IP-10, and RANTES from Maps 0–8, respectively. Using this panel of nine genes selected by a heuristic process for maximal proximity to centroid values, cluster analysis produced a dendrogram that differentiated the rejecting and nonrejecting groups with a similar pattern of hierarchical clustering as the complete panel of 60 genes (Fig. 4 ). These results demonstrate the power of SOM to segregate groups of genes with similar patterns of expression into maps that can be used to select small numbers or even a single gene from each subset to provide diagnostic power.



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  		Figure 4. Dendrogram of rejection profile in allograft recipients treated with costimulatory blockade of CD40L based on a single gene selected from each SOM. A single gene was selected from each of the nine subsets of genes defined by the SOM from Table 3 based on proximity to the mean value of expression. The nine selected genes were then analyzed by Cluster and Tree software to produce the dendrogram of gene expression from graft hearts of MR1-treated, allogeneic, and alymphoid recipients at days 1 and 7 and MR1-treated samples at the time of rejection (days 33 and 32). Controls are untransplanted BALB/c and B6 hearts. The degree of dissimilarity is proportional to the total length of the horizontal axis among groups.

 
To investigate if changes in mRNA expression correlated with changes in levels of protein, we analyzed two markers, CD4 and CD8, by immunohistochemistry (Fig. 5 ). These results show marked increases in CD8 in the allogeneic group. In contrast, expression of CD8 in the treated group with CD40L blockade indicates decreased CD8 expression. Both groups have low levels of CD4 expression.



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  		Figure 5. Immunohistochemistry of CD4 and CD8. Expression was visualized by indirect immunohistochemistry of CD4 in untransplanted control (a), allogeneic graft heart day 7 (b), MR1-treated graft heart day 7 (c), and MR1-treated graft heart day 33 (d), and of CD8 in untransplanted BALB/c control (e), allogeneic graft heart day 7 (f), MR1-treated graft heart day 7 (g), and MR1-treated graft heart day 33 (h).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
A major focus of our study was to investigate strategies of data reduction using clustering algorithms and SOM in a model of allograft rejection analyzing treatment of the CD40L blockade. Previous studies have shown that the CD40L blockade prolongs graft survival in murine and primate models [9 10 11 ]. In our study, using an iterative algorithm to generate SOM, we have identified subsets of genes that are expressed differentially in acute rejection after blockade of CD40 costimulation compared with control grafts. For example, genes up-regulated at the time of rejection, predominantly in the treated group, include F4/80, MCP-1, TGFß1, and TGFß3 among others. The up-regulation of F4/80 (a marker predominantly expressed on macrophages) [17 ] and MCP-1 [18 ] suggests a prominent role for macrophages in the delayed-rejection response. The increased levels of TGF-ß may be produced in response to chronic injury and, at least in part, by the graft [19 , 20 ]. Importantly, increased infiltration by macrophages and production of TGF-ß have been demonstrated previously in chronic graft rejection [21 ], suggesting that rejection following CD40L blockade with MR1 treatment has components consistent with chronic rejection. In addition, our analysis using SOM also identified increased expression of CCR1, CCR2, and CCR5 in the grafts from the treated recipients. All three of these chemokine receptors can be expressed by macrophages (in addition to other cell types), which is consistent with our evidence for increased infiltration by macrophages. Future studies will be needed to determine the effects of the CD40L blockade versus the delayed kinetics of rejection in the treated recipients on differential gene expression at the time of rejection.

In contrast, acute rejection in the untreated grafts showed increased levels of T-cell markers including TCR{alpha} and CD3{varepsilon}. Because CD8{alpha} and CD8ß, but not CD4, were up-regulated, the cells are most likely CD8 T cells, which was confirmed by immunohistochemistry. In addition, several chemokines including RANTES, IP-10, and Ltn were up-regulated in the untreated, acutely rejecting grafts. Thus, our study formulates an approach using clustering algorithms to analyze a panel of candidate genes to determine the molecular basis of the allogeneic immune response in an in vivo model of vascularized, solid organ transplantation.

Currently, a clinical diagnosis of allograft rejection is often based on histological analysis of biopsy specimens. For example, pathologic evaluation of kidney-allograft rejection is based on standardized Banff criteria that score intimal arteritis and tubulitis [22 ]. However, despite standardized, quantitative criteria of acute rejection in clinical biopsies, it is apparent that there are different mechanisms of rejection based on different clinical courses and varying responses to antirejection therapy that are not differentiated by histology. Some studies using polymerase chain reaction technologies have analyzed small subsets of genes that are up-regulated in rejecting allografts [23 , 24 ], suggesting the possibility of identifying a molecular profile of rejection. Although the regulation of protein levels can differ from mRNA expression [25 ], previous studies have shown that the analysis of the level of expression of a panel of mRNAs can be adequate to differentiate biological processes [26 27 28 29 ]. Our study suggests that future clinical studies may be able to define clinical states, such as acute rejection, based on small panels of up-regulated genes, which include a representative gene from each subset determined by SOM.

Dendrograms analyzing the kinetics of rejection in the untreated allogeneic recipients clearly segregated the response into early (days 1 and 3) and late (days 5 and 7) phases. One subset of genes was up-regulated only during the late phase and only in rejecting grafts (not in the syngeneic or alymphoid controls). In addition to markers of CD8 T cells, the most highly up-regulated gene in this subset was RANTES. Thus, robust up-regulation of genes in this map was only observed in the presence of an allogeneic-adaptive immune response. In contrast, most genes up-regulated during the early phase had increased expression in the syngeneic and alymphoid, as well as the allogeneic, recipients. Thus, up-regulation of genes in this map occurred independently of adaptive immunity, presumably triggered by injury or innate immunity. Our study did not identify qualitative differences in gene expression between the syngeneic and alymphoid recipients, indicating that NK cells were not required for the induction of the genes in our panel. This does not exclude the possibility that NK cells were activated in some experiments or that other genes not analyzed in our study were up-regulated by NK cells. Nevertheless, these results suggest that stimuli, such as ischemia/reperfusion or stress, were pivotal in up-regulating gene expression. The most highly up-regulated member of this map was MIP-2, a chemokine that has been shown previously to be up-regulated by ischemic injury [30 , 31 ].

Our results determined unique profiles of gene expression in different phases and types of allograft rejection, suggesting that our analysis could detect different molecular bases of rejection. By sorting the patterns of gene expression into maps using SOM, our analysis demonstrated that the different stages and types of rejection could be differentiated with a subset of only nine genes (CD4, CCR3, CCR5, LTß, MIP-1{alpha}, MIP-2, CD8{alpha}, IP-10, and RANTES). Our study focused on mRNA expression using RPA as a result of the technical feasibility of analyzing greater numbers of genes. Using immunohistochemistry, we confirmed correlations between mRNA and protein expression of CD4 and CD8. In addition, correlation between mRNA expression and function (kinetics of graft rejection) suggests changes in expression of functional proteins for at least a portion of our parameters. Future studies using gene-chip technology could greatly expand the database of gene expression and determine the number and specific subset of genes necessary to optimize the diagnostic criteria. However, one of the intriguing aspects of our study was the demonstration that a small number of genes (only nine in our study), if appropriately selected by SOM, were sufficient to distinguish different forms of rejection. The ability to reduce the amount of data necessary to differentiate rejection states using molecular profiles of rejection has important practical implications for the diagnosis of graft rejection and the development of individualized, therapeutic regimens.


   ACKNOWLEDGEMENTS
 
This work was supported by grants by the American Heart Association Established Investigator Award, Arthritis Foundation, and National Institutes of Health (AI44085 to D. L. P.). We thank R. Noelle for the MR1 mAb (Dartmouth College, Hanover, NH), M. B. Eisen for the Cluster and Tree software (Lawrence Livermore Radiation Laboratory, Berkeley, CA), S. Golub for the GeneCluster software (Dana Farber Cancer Institute, Boston, MA), Min Xu for technical support, and Charles Carpenter, Nicholas Tilney, Patricia Finn, Thomas Mueller, Charlotte McKee, and Christian Schroeter for critical review of the manuscript.

Received June 18, 2001; revised September 21, 2001; accepted October 15, 2001.


   REFERENCES
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 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
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