Journal of Leukocyte Biology Myeloid cells, immune suppression, tumor immunology
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(Journal of Leukocyte Biology. 2003;73:379-390.)
© 2003 by Society for Leukocyte Biology

Differentiation of stress, metabolism, communication, and defense responses following transplantation

Thomas F. Mueller*, Chunyan Ma*, James A. Lederer{dagger} and David L. Perkins*

* Laboratory of Molecular Immunology, Department of Medicine, and
{dagger} Laboratory of Immunology, Department of Surgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts

Correspondence: Dr. David L Perkins, Laboratory of Molecular Immunology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115. E-mail: dperkins{at}rics.bwh.harvard.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The biological complexity of allograft rejection and alloantigen-independent mechanisms is poorly understood. Therefore, we analyzed four components of the biological response following transplantation by global gene analysis. A comparative and kinetic approach was used to identify gene expression profiles. Biological processes were assigned to genes displaying the largest alterations in expression. Metabolism, stress response, and cell organization were the predominant, biological processes associated with ischemia and systemic stress. Innate and adaptive immune responses induced a transcriptional shift toward defense and cell communication. The kinetic analysis showed a shift from innate toward adaptive responses in the post-transplant course.

Key Words: molecular biology • gene regulation • rodent


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The biological complexity of allograft rejection, including alloantigen-independent and adaptive-immune mechanisms, is not well understood. Many studies analyzing graft survival in various knockout (KO) strains showed a prolonged but nevertheless limited graft survival. Recent studies have shown that antigen-independent events can enhance proinflammatory responses and decrease graft survival. Factors such as ischemia, reperfusion, surgical injury, systemic stress, and donor brain death can decrease graft survival [1 , 2 ]. Recent studies from our laboratory demonstrated a robust, innate immune response following transplantation, including up-regulation of multiple chemokines, cytokines, and proinflammatory mediators in a murine cardiac transplant model with a complete absence of functional lymphocytes [3 , 4 ]. The mechanisms by which antigen-independent factors modulate the inflammatory response and decrease graft survival remain poorly understood. Nontransplant models have shown that antigen-nonspecific innate immune signals derived from pathogens are important or even necessary to induce the antigen-specific, adaptive immune response mediated by lymphocytes [5 , 6 ]. Following transplantation, it is apparent that noninfectious factors, possibly including ischemia, systemic stress, and other antigen-independent mechanisms, can activate innate immune responses.

To study these complex biologic phenomena, we used DNA microarrays with results confirmed by real-time polymerase chain reaction (PCR) to compare patterns and kinetics of gene-expression profiles of ischemia, stress, and innate and adaptive immune responses following transplantation. Considering complexity, pleiotropy, and redundancy in the immune system, our objective was not to filter for a single gene or gene family but to analyze global patterns of gene expression. We used hierarchical clustering algorithms and self-organizing maps (SOMs) to identify patterns of gene expression. In addition, the Gene Ontology (GO) database was used to assign core biological processes associated with the genes differentially regulated following transplantation [7 , 8 ].

It is interesting that our analyses demonstrate disproportionate expression of genes from categories of biological processes modulated by ischemia, systemic stress, and innate and adaptive immunity.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Mice
Eight- to 12-week-old male mice, including BALB/cByJ (BALB/c; H-2d), C57BL/6J (B6; H-2b), C57BL/6J-RAG-1tm1Mom (B6-RAG-/-; H-2b; JAX, Bar Harbor, MA), and BALB/c-AnNTac-RAG2tm1N12 (BALB/c-RAG-/-; H-2d; Taconic, Germantown, NY), were used as donors and recipients in the transplant experiments. All microarray experiments were run in duplicate and the real-time reverse-transcriptase (RT)-PCR experiments in triplicate, analyzing samples from three animals per group. Mice are maintained in vented racks with constant temperature and humidity in our animal facility under virus antibody-free conditions.

Vascularized heterotopic cardiac transplantation
Murine hearts were transplanted as described previously [9 ]. Briefly, hearts were harvested from freshly killed donors and immediately transplanted into 8- to 12-week-old recipients 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 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. Donor grafts were harvested at 24 h or 7 days following transplantation and were divided into equal sections for preparation of RNA and tissue sections for histology. Altogether, organs from 37 animals were harvested.

DNA microarrays
Experimental procedures for the microarrays (GeneChip) were performed according to the Affymetrix (Santa Clara, CA) GeneChip Expression Analysis technical manual. Briefly, double-stranded cDNA was synthesized by means of the SuperScript Choice system (Gibco-BRL, Life Technologies, Rockville, MD) and a T7(dT) 24 primer (Gensetoligos, La Jolla, CA). The cDNA was purified using phenol/chloroform extraction with Phase Lock Gel (Eppendorf, Germany) and was 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 double-stranded cDNA, which was synthesized. The biotinylated RNA was cleaned with an RNeasy mini kit (Qiagen, Valencia, CA), fragmented to 50–200 nt, and then hybridized to an Affymetrix murine array (Mu11kB), which contains probe sets for 6500 genes and expressed sequence tags (ests). After being washed, the array was stained with streptavidin-phycoerythrin (Molecular Probes, Eugene, OR), amplified by biotinylated antistreptavidin (Vector Laboratories, Burlingame, CA), and then scanned on a HP Genearray scanner. The intensity for each feature of the array was captured with Affymetrix GeneChip software, according to standard Affymetrix procedures. Correlation coeffecient of duplicate control heart microarray results was r = 0.98.

Statistics and data analysis
Array data were analyzed with Microarray Suite 4.0.1. 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, which allows the direct subtraction of background and cross-hybridization signals. Each array was normalized to a standard of 2500 units per probe set. To determine the quantitative RNA level, the average of the differences representing PM – MM for each gene-specific probe set was calculated. In addition the expression of each probe set was qualitatively categorized as present, marginal, or absent. Calculations of means and variances were performed with JMP statistical software (SAS Institute, Cary, NC).

Cluster analysis
Hierarchical clustering, using Cluster and TreeView software [10 ] (courtesy of Michael B. Eisen, Lawrence Livermore Radiation Laboratory, Berkeley, CA), and SOMs, using GeneCluster software [11 ] (courtesy of Whitehead Institute for Biomedical Institute, Cambridge, MA), were applied to analyze gene expression.

Average difference values were analyzed by Cluster using the hierarchical clustering algorithm with average linkage clustering. Briefly, dissimilarity of gene expression between each experimental group is calculated by Pearson correlation relationships and visualized by a tree diagram (dendrogram) [10 ]. Branch lengths represent the degree of similarity between the groups.

A second algorithm, SOMs, was used to cluster genes with similar expression patterns across all experimental groups. Genes showing a less than twofold change in relative expression across the experimental groups were eliminated, and the expression level of each gene was normalized across the experiments. The remaining genes were analyzed by GeneCluster using a 2 x 3 geometry, and six clusters were generated, eliminating clusters with few genes or large standard deviations [11 ]. Each cluster contained genes with similar patterns of gene expression across the experimental groups. 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.

Real-time RT-PCR
Primer pairs were designed using Primer Express software (Applied Biosystems, Foster City, CA). Forward and reverse primers were chosen to have a length between 18 and 22 base pairs and were designed to amplify an amplicon length of 51 base pairs. The housekeeping gene glyceraldehyde 3-phosphate dehydrogenase (GAPDH; M32599) was selected as endogeneous control. All primer pairs were tested in immune-rich tissue samples and nontemplate controls for specificity, primer–dimer conformation, and reproducibility. For the real-time RT-PCR analyses, a coefficient of variance between 0.2 and 1.2% was obtained measuring GAPDH in different runs of identical replicates in the various groups. The sequences of the forward (FW) and reverse (RE) primer pairs used in the experiments are as follows: major histocompatibility complex (MHC) class II invariant (Ii; FW) GAAGCTTCCGAAATCTGCCA, (RE) GGAGTAGCCATCCGCATCTG; MHC class II antigen (I-A-b; FW) TTCATGGGCGAGTGCTACTTC, (RE) CACATATCGTATGCGCTGCG; C1q complement (C1q)-A (FW) CTGGCATCCGGACTGGTATC, (RE) CAGATTCCCCTGGGTCTCCT; C1q-B (FW) AGGACCATCAACAGCCCCTT, (RE) CTTTTCGAAGCGAATGACCTG; C1q-B (FW) TGTGGAGGGCCGATACAAAC, (RE) CGGGTGACTGTGAATACCGA; acute-phase proteins (APP; FW) GGGCTGACAAACATCAAGACG, (RE) TGCATCCATCTTCACTTCCG; lymphocyte function-associated antigen-1 (LFA-1; FW) CAGCCATCTGCCTATGACCA, (RE) CCCAACCAAGGCCTCTAGTGT; and GAPDH (FW) TGTGGAAGGGCTCATGACC, (RE) TCTTCTGGGTGGCAGTGATG.

RNA for the real-time RT-PCR studies was obtained from separate tissue samples to control for technical and biological variability. Total murine RNA was isolated from three hearts per experimental group comprising untransplanted BALB/c hearts (control group), day 1 B6 graft hearts (syngeneic group), day 1 allogeneic BALB/c-RAG-/- graft hearts (RAG-deficient group), and days 1 and 7 BALB/c graft hearts (allogeneic group). TRI reagent (Sigma-Aldrich, St. Louis, MO) was used for the RNA isolation, and samples were treated with DNase to eliminate DNA (DNase I, Amplification Grade, Invitrogen Life Technologies, Carlsbad, CA). The RNA was reverse-transcribed using SuperScript II RNase RT (Gibco, Carlsbad, CA). The GeneAmp 5700 sequence detection system (Applied Biosystems) was used to perform real-time PCR using 250 ng template cDNA, 5 µM FW and RE primer, and 10 µL 10x SYBR Green PCR Master Mix (Applied Biosystems) per well in a MicroAmp Opitcal 96-well reaction plate (Applied Biosystems). The gene-specific PCR products are continuously measured by the increase in fluorescence as a result of the binding of SYBR Green to double-stranded DNA during 40 cycles. Dye ROX, included in the SYBR Green PCR Master Mix, was used as a passive reference to normalize for non-PCR-related fluctuations in fluorescence signal. The cycle thresholds (CT) of each target gene are relative to the CT value of the endogeneous reference, GAPDH. The differences in the CT values between GAPDH and each single target gene, respectively, are calculated as fold-change in expression relative to GAPDH.

Gene classification
All genes selected by the SOMs were classified according to the biological processes to which they contribute using the GO (<www.geneontology.org>) annotation system [7 ]. The GO identifiers were obtained from the mouse genome informatics database [12 ]. The biological process is defined as the biological objective to which the gene contributes. Most genes could be assigned to the following five broad processes: cell communication (communication; GO:0007154), cell organization and biogenesis (organization; GO:0007010), metabolism (GO:0008152), defense response (defense; GO:0006952), and stress response (stress; GO:0006950). The remaining genes, in particular those with unknown biological process, were classified under "other". If a gene contributed to more than one distinct biological process, the most appropriate to the underlying experimental settings was chosen. A detailed description of each gene, including identifier, standard name, characterization, and relevant reference as well as related biological process according to the GO classification, is provided on our website (<perkinslab.bwh.harvard.edu>).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Experimental strategy
To investigate the complex process of allograft rejection, we used DNA microarrays to monitor transcriptional profiles following transplantation in a murine model of vascularized solid organ transplantation. Our experimental groups were designed to examine four distinct components of the biological response, including ischemia, systemic stress, and innate and adaptive immunity. First, to detect changes in gene expression as a result of ischemia, freshly harvested BALB/c hearts were incubated in 1% O2. Although in vitro incubation could eliminate humoral and neural signals triggered in vivo, the important advantage of in vitro culture was the elimination of effects on mRNA levels as a result of surgical stress and transplant injury, which could confound our analysis of stress (see below). Second, to investigate the effect of systemic stress, we analyzed the native heart in transplant recipients. In the heterotopic cardiac transplant model, the graft heart is transplanted into the abdomen, whereas the recipient’s native heart is not surgically manipulated and remains in the thorax. Thus, the native heart is exposed to all of the systemic mediators of stress induced by the surgical procedure but not to the effects of surgical wounding or ex vivo ischemia. Third, to identify changes in gene expression induced by innate-immune responses, we transplanted allogeneic grafts into RAG KO recipients (BALB/c-RAG-/- into B6-RAG-/-). In this alymphoid group, the donor and recipients lack functional T and B lymphocytes and are incapable of mounting an adaptive immune response. In addition, we included a second group, syngeneic recipients (B6 into B6), to analyze the innate response. In both innate groups, gene expression should be regulated by antigen-independent signals. Fourth, to detect genes modulated by adaptive immunity, we analyzed transplants in wild-type allogeneic recipients (BALB/c into B6) at days 1 and 7 following transplantation. As the graft in the experimental groups could be exposed to multiple components of the response, including ischemia, stress, or innate stimuli, all genes significantly modulated by these signals were masked from subsequent analyses. Based on these criteria, we could experimentally assign changes in gene expression to four components of the response operationally correlated with ischemia, stress, and innate or adaptive immunity.

Cluster analysis of experimental groups at days 1 and 7
To determine the global relationships among the experimental groups at day 1, we analyzed expression profiles from DNA microarrays of allogeneic graft heart, alymphoid graft heart, native heart, ischemic heart, and untransplanted control heart using agglomerative, hierarchical clustering algorithms that depict the degree of dissimilarity in dendrograms [10 ] (Fig. 1a ). Also, we analyzed untransplanted control LN to include an immune gene-rich sample. As shown in the dendrogram, the three untransplanted groups (control, native, and ischemic hearts) were the most similar; also, the two transplanted groups (allogeneic day 1 and alymphoid day 1) were only modestly dissimilar from each other. As expected, the alymphoid and syngeneic day 1 groups were highly similar (not shown). Gene expression in the LN was the most dissimilar compared with all five heart groups. These observations show that the changes in gene expression at day 1 following transplantation are not highly dissimilar in the allogeneic versus alymphoid group, suggesting that gene regulation is predominantly a result of antigen-independent mechanisms.



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  		Figure 1. Dendrogram of gene expression following transplantation. (a) Depicts the expression profiles at day 1 (d1) of allogeneic graft heart, alymphoid graft heart, native heart, ischemic heart, untransplanted control heart, and lymph node (LN). (b) Indicates the dendrogram analysis at days 1 and 7 (d1 and d7) of allogeneic graft hearts following transplantation and untransplanted control heart and LN. x-Axis distance is proportional to the dissimilarity between groups.

 
Using the hierarchical clustering algorithm, we performed a kinetic analysis of the adaptive alloimmune response of allogeneic graft hearts at days 1 and 7 following transplantation, plus control untransplanted heart and LN (Fig. 1b) . In our experiments, median allogeneic graft survival was 8 days (not shown); thus, day 7 narrowly precedes the time of rejection. The dendrogram shows that at day 7, there is markedly increased similarity with the LN, whereas the day 1 group remains more similar with control, untransplanted heart. These results suggest that the expression of genes composing the adaptive component are highly up-regulated at day 7 but not at day 1 following transplantation.

Gene modulation associated with antigen-independent mechanisms
Genes (181) were identified using a selection threshold of a greater than twofold change in relative expression across the experimental groups. These genes are listed in rank order by the absolute magnitude of increased or decreased expression, respectively (Table 1 ). Supplementary information to each of the 181 modulated genes is available at our website (<perkinslab.bwh.harvard.edu>).


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  		Table 1. Genes Modulated As a Result of Ischemia, Stress, and Innate and Adaptive Immune Responses

 
To identify the specific genes comprising the antigen-independent components of the post-transplant response, we used SOMs [11 ]. Analyzing the expression data of tissue samples from the control, ischemic, and native hearts and syngeneic and alymphoid graft recipients produced six clusters of genes with distinct profiles of expression (Fig. 2 ). Each cluster comprised genes with a similar pattern of expression across the five experimental groups. Cluster 0 includes 21 genes down-regulated in all groups relative to control levels; conversely, cluster 5 contains 30 genes that are up-regulated in all experimental groups relative to control. Thus, modulation of genes in clusters 0 and 5 may be in response to general stress signals induced in all of the experimental conditions but not in the control heart.



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  		Figure 2. Gene modulation associated with alloantigen-independent mechanisms. Genes showing significant modulation were grouped into six clusters (0–5), each comprising genes with similar patterns of expression across the five experimental groups: control heart ({diamondsuit}), ischemic heart ({circ}), native heart ({blacktriangleup}), alymphoid graft heart day 1 (•), and syngeneic graft heart day 1 ({blacksquare}). Each cluster is represented by the centroid (average pattern) for genes in the cluster. Mean relative expression levels are shown on the y-axis and experimental groups on the x-axis, with error bars indicating 2 SD. The number of genes in each cluster is 21 (0), 16 (1), 15 (2), 12 (3), 19 (4), and 30 (5).

 
Genes modulated by ischemia
In the ischemia group, clusters 1, 2, and 5 contain up-regulated genes, and cluster 3 contains down-regulated genes (Fig. 2) . We identified 53 genes that are listed in rank order by the absolute magnitude of increased expression and 10 genes with decreased expression (Table 1) . Each gene was categorized according to the biological process using the GO database [7 ]. In the GO database, the biological process is a category defined by the biological objective of a gene product. Major biological processes detected in our study include defense, metabolism, stress, communication, and cell organization. Consistent with our experimental conditions, graphical analysis shows that the majority of genes modulated by ischemia are associated with changes in metabolism or cell organization (see Fig. 4A ). The subset of metabolism predominantly includes genes of electron transport, carbohydrate metabolism, and biosynthesis. The subset of cell organization comprises mainly ribosomal and cytoskeletal proteins.



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  		Figure 4. Biological processes mediated by the modulated genes. All significantly modulated genes of the groups ischemia (A), systemic stress (B), and innate (C), and adaptive (D) immunity are classified into six categories of biological processes including metabolism, defense, stress, cell organization, communication, and other. Each segment represents the percentage of assigned genes.

 
Genes modulated by systemic stress
Cluster 3 identified a subset of genes up-regulated in the native hearts (Fig. 2) . Using the same selection threshold and excluding genes up-regulated by ischemia, we identified a subset of 20 genes (Table 1) . Characterization of biological processes in cluster 3 shows an increase in the stress response category. It is interesting that this subset includes genes with the highest ratio of up-regulation (see Fig. 4B ). Genes mediating metabolic processes are, as in the ischemic hearts, an abundant group.

Genes modulated by innate immunity
Our experimental design includes two groups that lack an alloantigenic stimulus: the syngeneic group (B6 into B6) and the alymphoid group (BALB/c-RAG-/- into B6-RAG-/-). Cluster 4 of the SOM shows a subset of genes up-regulated in the syngeneic and alymphoid groups. In addition, cluster 5 includes genes up-regulated in the ischemic and native hearts, as well as the syngeneic and alymphoid graft hearts (Fig. 2) . A compilation of genes based on the same selection threshold up-regulated in the syngeneic and alymphoid groups but not in the ischemic or native hearts generates 38 genes (Table 1) . Based on the GO characterization, these genes predominantly belong to defense responses (see Fig. 4C ). They can be subclassified into immune response (for example, cytokines) or acute phase and inflammatory responses (for example, serum amyloid A 3). In addition, there is a large increase in the percentage of genes associated with communication, which includes predominantly adhesion molecules. Strikingly, 16% of all genes up-regulated in the innate experimental model were mediating cell-adhesion processes.

Genes modulated in the alloantigen-dependent, adaptive model
Based on our analysis with the clustering algorithms, the proinflammatory responses during the early phase following transplantation (day 1) were similar in syngeneic, alymphoid, and allogeneic recipients. In contrast, during the late phase (day 7), the allogeneic grafts produced a distinct profile (Fig. 1) . To investigate the antigen-dependent, adaptive immune response during rejection, we analyzed allogeneic groups from days 1 and 7 following transplantation using SOMs (Fig. 3 ). In this analysis, we also included untransplanted control heart, alymphoid graft heart, and LN. The analysis with SOMs shows the absence of a cluster-containing genes up-regulated exclusively at day 1 in the allogeneic group, which is consistent with the dendrograms produced by hierarchical clustering showing similarity among all of the groups of transplanted hearts at day 1. In contrast, a unique subset of genes was identified with modulated expression at day 7 in the allogeneic group. Specifically, cluster 2 contains genes up-regulated at day 7 in the allogeneic group and also highly expressed in LN. In addition, cluster 4 contains genes up-regulated at day 7 but not in LN or other groups. Using the same threshold criteria, compilation of genes significantly modulated in the allogeneic group at day 7 but not in the ischemic, native heart, or alymphoid groups produced 49 up-regulated and 11 down-regulated genes (Table 1) . Based on the GO classification, the majority of genes in this subset is associated with the defense response (Fig. 4D ). The 20 genes showing the highest up-regulation include mostly genes linked to the MHC or TCR complexes or to the complement cascade (Table 1) . Importantly, the percentage of the defense response is greatest in the adaptive group. It is interesting that in the innate and adaptive model adhesion molecules, classified in the segment communication, constitute a large group. Conversely, the metabolic and stress response components are reduced in the adaptive group.



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  		Figure 3. Gene modulation in the alloantigen-dependent, adaptive model. Genes showing significant modulation were grouped into six clusters (0–5), each comprising genes with similar patterns of expression across the five experimental groups: control heart ({diamondsuit}), alymphoid graft heart day 1 ({circ}), allogeneic graft heart day 1 ({blacktriangleup}), allogeneic graft heart day 7 (•), and untransplanted control LN ({blacksquare}). Each cluster is represented by the centroid (average pattern) for genes in the cluster. Mean relative expression levels are shown on the y-axis and experimental groups on the x-axis, with error bars indicating 2 SD. The number of genes in each cluster is 44 (0), 33 (1), 28 (2), 18 (3), 29 (4), and 23 (5).

 
Confirmation of microarray data by real-time RT-PCR
To corroborate the microarray data, a group of genes showing a distinct profile of up-regulation in the allogeneic grafts during the late phase (day 7) and no significant change during the early phase (day 1) in allogeneic, syngeneic, and alymphoid grafts (Fig. 5A ) were selected for further analysis by real-time RT-PCR. As shown in Figure 5 , the PCR data reflect the expression profiles found for these genes by cluster analysis and SOMs. In particular, the MHC genes Ii and I-A-b display the highest increases in expression in the allogeneic grafts on day 7, in contrast to their levels in the day 1 syngeneic, alymphoid, and allogeneic cardiac grafts (Fig. 5B) . It is interesting that, as shown by the microarray data, the strong adaptive response during rejection is associated with an increase in expression of genes related to the complement cascade. In addition, markers of the biological processes, APP and cell adhesion (LFA-1), are involved in the late allogeneic response, but not exclusively and to a lesser degree.



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  		Figure 5. Changes in expression levels of selected genes. The magnitude of absolute increase or decrease in expression obtained by the analysis of the microarray data (A) versus the fold-change in expression relative to the endogeneous reference GAPDH obtained by real-time RT-PCR (B) is shown for seven target genes. The gene expression was measured in untransplanted control hearts (Co) on day 1 following transplantation in donor graft hearts B6 (syng d1), RAG-/- graft hearts (RAG-/-d1), BALB/c graft hearts (allog d1), and on day 7 BALB/c graft hearts (allog d7).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Our experimental approach was based on the assumption that the modulation of gene expression following transplantation is a complex process regulated by multiple biological components including ischemia, systemic stress, and innate and adaptive immunity. To produce an overview of the biological relationships, we investigated four experimental groups with dendrograms generated with hierarchical clustering algorithms that calculated dissimilarity based on the sum of Pearson correlation coeffecients of the 6500 gene expression values (Fig. 1) . The dendrograms showed distinct expression profiles among the different experimental groups. The kinetic analysis showed a high level of dissimilarity between the early (day 1) and late (day 7) phases of the rejection response in the allogeneic group. The day 7 allogeneic group is most similar to control LN. This observation is consistent with the development of an antigen-specific response during the late phase, which induces the expression of genes within the graft that is constitutively expressed in LN tissue. In contrast to the similarity between the allogeneic day 7 and LN groups, the alymphoid and syngeneic day 1 groups had a high degree of dissimilarity compared with LN. This observation is consistent with the antigen-independent nature of the early response. Thus, the dendrograms differentiate the early phase, which is predominantly antigen-independent, from the late phase, which is predominantly antigen-dependent [1 , 3 , 13 ].

To identify specific subsets of modulated genes with similar expression patterns during the late or adaptive phase of rejection, we used SOMs (Figs. 2 and 3) . This clustering technique has been successfully applied to high-dimensional and nonlinear problems in many areas of science. Supplementary information on all selected genes of the experimental groups including accession number, characterization, and relevant references is provided in our website (<perkinslab.bwh.harvard.edu>). Specific genes identified with the SOMs included five MHC genes, four antigen receptor genes, four complement genes, three antigen-processing genes, multiple cell-surface markers, and adhesion molecules. In addition, a T cell surface marker (Thy-1) and an IFN–IP-30 were detected [14 ]. This subset indicates that a major component of the late response belongs to adaptive immunity.

The method of real-time RT-PCR was used as a validation method, and specifically, genes up-regulated during this late adaptive response were selected for corroboration. Despite the methodological differences between PCR amplification and microarray hybridization and the use of different RNA samples, the seven target genes show comparable expression profiles for both methods. In particular, genes associated with the adaptive immune response display the highest degree of up-regulation during the allogeneic response. In addition, confirming the findings by cluster analysis and GO attribution, this allogeneic response is associated with increased expression of markers associated with innate and inflammatory processes such as complement factors.

Our analysis of the early response following transplantation focused on three components including ischemia, systemic stress, and innate immunity. The ischemic response, which was induced in vitro to eliminate confounding effects of systemic mediators or infiltrating cells, contained 63 genes that were identified using SOMs. It is interesting that five genes (elongation factor, gelsolin, ATP synthase, crystallin, and ubiquitin) up-regulated by ischemia were also up-regulated in a previous report analyzing congestive heart failure [15 ]. Importantly, most of the additional genes identified in our ischemia group have previously reported functions consistent with a functional role during ischemia or injury induced by ischemia. For example, a recent study demonstrates that hypoxia up-regulates expression of ferritin by decreasing promoter binding of the inhibitory iron-regulatory protein [16 ]. MnSOD, which protects against reactive oxygen species, has been shown to be transcriptionally up-regulated by hypoxia and lipopolysaccharides (LPS) [17 , 18 ]. C1 esterase inhibitor is protective against myocardial ischemia and cold ischemia during organ preservation [19 , 20 ]. Crystallin, related to the small hsp, including hsp27, is induced by stress and is highly expressed in encysted artemia franciscana (brine shrimp), which can survive 3 years of anoxia [21 , 22 ]. In addition, nine genes highly expressed in cardiac tissue were down-regulated in response to ischemia.

Our experimental model included a group to analyze gene expression by systemic stress. The graft heart is transplanted heterotopically into the abdomen, whereas the recipient’s native heart is not surgically manipulated but is exposed to the systemic stresses of the transplant procedure. Our analysis of the native heart identified 21 modulated genes using SOMs. Supporting the conclusion that these genes were, at least in part, regulated by stress is the observation that multiple genes in this subset are regulated by other types of stress. For example, MT-I and SelP are up-regulated by exposure to heavy metals. It is interesting that MT has been shown to provide protection against electrophiles and oxidant stress and to be induced during acute liver injury and regeneration [23 , 24 ]. The ryanodine receptor, a Na/Ca exchanger, binds calmodulin and FKBP12 and is sensitive to redox status and mechanical stress [25 , 26 ]. 24p3, a gelatinase-associated lipocalin, is an acute-phase response protein that binds formylpeptides and LPS and has been suggested to function as a regulator of inflammation [27 ]. EF-2 is up-regulated by angiotensin II or ß-adrenergic agonists, both of which could be released by the transplant surgery [28 , 29 ]. 11 ß-Hydroxysteroid dehydrogenase is an important deactivator of glucocorticoids [30 ], and 1-8U is an IFN-inducible protein [31 ]. CD36, a lipoprotein receptor expressed by multiple cell types including endothelial and red blood cells, can mediate cell adherence via thrombospondin [32 , 33 ]. We detected only a single est showing down-regulation in response to stress. Together, these observations demonstrate a major stress response following transplantation that is independent of ischemia and immunity.

In addition to ischemia and systemic stress, our experimental design analyzed antigen-independent modulation of gene expression in two groups without antigen-dependent responses, the syngeneic and alymphoid (RAG-/-) groups, which were operationally defined as innate groups. Our analysis identified 38 genes up-regulated by antigen-independent signals but independent of ischemia or systemic stress. In this subset, IFN-ß, a cytokine shown to be important in innate-immune responses, was highly up-regulated [34 ]. Serglycin, a widely distributed proteoglycan highly produced by endothelial cells, binds the leukocyte receptor CD44 [35 , 36 ]. Osteopontin, a cytokine produced by multiple cell types including chrondocytes, has been shown to promote T helper cell type 1 responses [37 ]. Serum amyloid A 3, up-regulated by glucocorticoids, is a mediator of the acute-phase response [38 ]. Prothymosin {alpha}, which has receptors on monocytes, promotes cellular proliferation and enhances protective immune responses induced by vaccination [39 , 40 ]. Apo E is a glycosylated protein with multiple functions. It is interesting that mice deficient in Apo E have increased susceptibility to LPS endotoxemic shock [41 ]. Ly-6C, which is expressed on multiple cell types including macrophages, endothelial, natural killer (NK), and T cells, has been associated with NK cell cytotoxicity [42 43 44 45 ]. Cofilin is an actin-binding protein important in actin-filament depolymerization crucial in motility and capping [46 ]. The integrin ß subunit plays a fundamental role in cell division, differentiation, and cell migration by mediating cell–matrix and cell–cell interactions [47 ]. Calpactin I (annexin II) is induced by transforming growth factor-ß (TFG-ß) and is differentially expressed during wound repair [48 ]. hsp84 and hsp47 are stress-induced proteins. It is interesting that hsp47 binds collagen and fetuin and is associated with fibrosis and atheromatous plaques [49 , 50 ]. Endoglin, a TGF-ß-binding protein, is up-regulated by TGF-ß and overexpressed after arterial injury [51 , 52 ]. Activated macrophages can up-regulate the production of fibronectin, which has been shown to modulate TGF-ß induction of IL-8 [53 , 54 ]. TGF-ß stimulates TSC-22, a DNA-binding protein of the kruppel family [55 ]. We detected 10 ests but no known genes down-regulated in the innate-immune group. These results do not identify the specific triggers of the innate response, which include multiple, potential candidates, for example, complement activation, necrotic or apoptotic cells, and activation of thrombosis among others. However, our results demonstrate that transplantation injury, even in the absence of infection, can stimulate a robust innate-immune response.

Our study analyzed the expression of ~6500 genes and ests following transplantation and identified 181 genes with significantly modulated expression in at least one of the experimental groups of ischemia, stress, and innate or adaptive immunity. To understand the biological importance of changes in gene expression, we used the GO database to categorize the biological process of all modulated genes [7 , 8 , 10 ]. Remarkably, greater than 80% of all modulated genes were contained within only five categories of biological processes, including defense, stress, metabolism, communication, and cell organization. Supporting the validity of this approach, the relative contribution of each biological process changed in the experimental groups consistent with our hypotheses based on previous studies. For example, greater than 70% of genes modulated during ischemia were categorized in metabolism and cell organization, whereas only 3% were attributed to defense. In the stress group, there was a major increase in the stress (15%) and defense (15%) categories, and metabolism (40%) also remained a major category. In the innate response, the defense category (24%) increased further, whereas metabolism (18%) decreased. It is interesting that the communication category (18%), which includes genes of adhesion molecules and signal transducers, markedly increased during the innate response. This response is consistent with a critical role by innate immunity in the activation and recruitment of an inflammatory response. In the adaptive response, ~50% of the modulated genes are within the defense category. The categories of metabolism (18%), cell organization (8%), and communication (13%) remain substantial components, suggesting a significant contribution to an antigen-specific response.

The profiles of gene expression and associated biological processes provide a basis to differentiate major biological responses in transplantation. Despite the remarkable power of profiling gene expression with microarrays, some limitations are noteworthy. The DNA microarrays cover only a subset of the whole murine genome, the molecular and biological function of the ests is not known, and post-transcriptional changes are not detected. In particular, the genes and biological processes represented on the individual chip do not per se reflect their true distribution in the genome. In this respect, information provided by the gene chip producers on the relative distribution of genes representing the different biological processes would be most helpful. Using the GO annotations, approximately 6% of all genes on the Affymetrix array (Mu11kB) used in our study are related to the defense response, comprising immune and inflammatory genes. During the late allogeneic response, approximately 50% of all genes up-regulated were associated with the defense response, indicating a marked increase compared with the baseline of 6% on the whole chip. Thus, the underlying analysis shows characteristic shifts in the biological responses, but the inherent bias of gene selection by the individual chips always has to be taken into account.

We propose that the presented approach provides a framework for future studies investigating the complex responses induced by organ transplantation.

Understanding the interactions between the defense category, which is commonly the focus of transplant biologists, and other biological processes of metabolism, cell organization, and communication may increase our understanding of the rejection response in vivo and provide new targets for novel, therapeutic strategies.


   ACKNOWLEDGEMENTS
 
This work was supported by grants from the American Heart Association, Arthritis Foundation, and National Institutes of Health (AI44085 to D. L. P.). We are grateful to Richard Pratt for consultations with the microarray techniques and Patricia Finn, Charlotte McKee, and Kenneth Christopher for critical review of this manuscript. We thank Min Xu for technical support.

Received January 30, 2002; revised September 9, 2002; accepted September 24, 2002.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Lu, C. Y., Penfield, J. G., Kielar, M. L., Vazquez, M. A., Jeyarajah, D. R. (1999) Hypothesis: is renal allograft rejection initiated by the response to injury sustained during the transplant process? Kidney Int. 55,2157-2168[CrossRef][Medline]
  2. Gasser, M., Waaga, A. M., Laskowski, I. A., Tilney, N. L. (2000) The influence of donor brain death on short and long-term outcome of solid organ allografts Ann. Transplant. 5,61-67
  3. He, H., Stone, J. R., Perkins, D. L. (2002) Analysis of robust innate immune response after transplantation in the absence of adaptive immunity Transplantation 73,853-861[CrossRef][Medline]
  4. Christopher, K., Mueller, T. F., Ma, C., Liang, Y., Perkins, D. L. (2002) Analysis of the innate and adaptive phases of allograft rejection by cluster analysis of transcriptional profiles J. Immunol. 169,522-530[Abstract/Free Full Text]
  5. Horng, T., Barton, G. M., Medzhitov, R. (2001) TIRAP: an adapter molecule in the Toll signaling pathway Nat. Immunol. 2,835-841[CrossRef][Medline]
  6. Fox, A., Harrison, L. C. (2000) Innate immunity and graft rejection Immunol. Rev. 173,141-147[CrossRef][Medline]
  7. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., Sherlock, G. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat. Genet. 25,25-29[CrossRef][Medline]
  8. Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., Gocayne, J.D., Amanatides, P., Ballew, R.M., Huson, D.H., Wortman, J.R., Zhang, Q., Kodira, C.D., Zheng, X.H., Chen, L., Skupski, M., et al (2001) The sequence of the human genome Science 291,1304-1351[Abstract/Free Full Text]
  9. Corry, R. J., Winn, H. J., Russell, P. S. (1973) Heart transplantation in congenic strains of mice Transplant. Proc. 5,733-735[Medline]
  10. Eisen, M. B., Spellman, P. T., Brown, P. O., Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns Proc. Natl. Acad. Sci. USA 95,14863-14868[Abstract/Free Full Text]
  11. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., Golub, T. R. (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation Proc. Natl. Acad. Sci. USA 96,2907-2912[Abstract/Free Full Text]
  12. Blake, J. A., Eppig, J. T., Richardson, J. E., Davisson, M. T. (2000) The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. The Mouse Genome Database Group Nucleic Acids Res. 28,108-111[Abstract/Free Full Text]
  13. Halloran, P. F., Goes, N., Urmson, J., Ramassar, V., Hobart, M., Sims, T., Lui, S. L., Miller, L. W. (1997) MHC expression in organ transplants: lessons from the knock-out mice Transplant. Proc. 29,1041-1044[CrossRef][Medline]
  14. Killeen, N. (1997) T-cell regulation: Thy-1—hiding in full view Curr. Biol. 7,R774-R777[CrossRef][Medline]
  15. Yang, J., Moravec, C. S., Sussman, M. A., DiPaola, N. R., Fu, D., Hawthorn, L., Mitchell, C. A., Young, J. B., Francis, G. S., McCarthy, P. M., Bond, M. (2000) Decreased SLIM1 expression and increased gelsolin expression in failing human hearts measured by high-density oligonucleotide arrays Circulation 102,3046-3052[Abstract/Free Full Text]
  16. Kuriyama-Matsumura, K., Sato, H., Suzuki, M., Bannai, S. (2001) Effects of hyperoxia and iron on iron regulatory protein-1 activity and the ferritin synthesis in mouse peritoneal macrophages Biochim. Biophys. Acta 1544,370-377[CrossRef][Medline]
  17. White, J. E., Tsan, M. F. (2001) Differential induction of TNF-alpha and MnSOD by endotoxin: role of reactive oxygen species and NADPH oxidase Am. J. Respir. Cell Mol. Biol. 24,164-169[Abstract/Free Full Text]
  18. Ohman, T., Parish, G., Jackson, R. M. (1999) Hypoxic modulation of manganese superoxide dismutase promoter activity and gene expression in lung epithelial cells Am. J. Respir. Cell Mol. Biol. 21,119-127[Abstract/Free Full Text]
  19. Buerke, M., Murohara, T., Lefer, A. M. (1995) Cardioprotective effects of a C1 esterase inhibitor in myocardial ischemia and reperfusion Circulation 91,393-402[Abstract/Free Full Text]
  20. Bergamaschini, L., Gatti, S., Caccamo, L., Prato, P., Latham, L., Trezza, P., Maggioni, M., Gobbo, G., Fassati, L. R. (2001) C1 inhibitor potentiates the protective effect of organ preservation solution on endothelial cells during cold storage Transplant. Proc. 33,939-941[CrossRef][Medline]
  21. Smoyer, W. E., Ransom, R., Harris, R. C., Welsh, M. J., Lutsch, G., Benndorf, R. (2000) Ischemic acute renal failure induces differential expression of small heat shock proteins J. Am. Soc. Nephrol. 11,211-221[Abstract/Free Full Text]
  22. Clegg, J. S., Jackson, S. A., Popov, V. I. (2000) Long-term anoxia in encysted embryos of the crustacean, Artemia franciscana: viability, ultrastructure, and stress proteins Cell Tissue Res. 301,433-446[CrossRef][Medline]
  23. Theocharis, S. E., Kanelli, H., Margeli, A. P., Spiliopoulou, C. A., Koutselinis, A. S. (2000) Metallothionein and heat shock protein expression during acute liver injury and regeneration in rats Clin. Chem. Lab. Med. 38,1137-1140[CrossRef][Medline]
  24. Liu, S., Kawai, K., Tyurin, V. A., Tyurina, Y. Y., Borisenko, G. G., Fabisiak, J. P., Quinn, P. J., Pitt, B. R., Kagan, V. E. (2001) Nitric oxide-dependent pro-oxidant and pro-apoptotic effect of metallothioneins in HL-60 cells challenged with cupric nitrilotriacetate Biochem. J. 354,397-406[CrossRef][Medline]
  25. Sun, J., Xu, L., Eu, J. P., Stamler, J. S., Meissner, G. (2001) Classes of thiols that influence the activity of the skeletal muscle calcium release channel J. Biol. Chem. 276,15625-15630[Abstract/Free Full Text]
  26. Ruwhof, C., van Wamel, J. T., Noordzij, L. A., Aydin, S., Harper, J. C., van der Laarse, A. (2001) Mechanical stress stimulates phospholipase C activity and intracellular calcium ion levels in neonatal rat cardiomyocytes Cell Calcium 29,73-83[CrossRef][Medline]
  27. Liu, Q., Nilsen-Hamilton, M. (1995) Identification of a new acute phase protein J. Biol. Chem. 270,22565-22570[Abstract/Free Full Text]
  28. McLeod, L. E., Wang, L., Proud, C. G. (2001) beta-Adrenergic agonists increase phosphorylation of elongation factor 2 in cardiomyocytes without eliciting calcium-independent eEF2 kinase activity FEBS Lett. 489,225-228[CrossRef][Medline]
  29. Everett, A. D., Stoops, T. D., Nairn, A. C., Brautigan, D. (2001) Angiotensin II regulates phosphorylation of translation elongation factor-2 in cardiac myocytes Am. J. Physiol. Heart Circ. Physiol. 281,H161-H167[Abstract/Free Full Text]
  30. Clark, A. M., Griswold, M. D. (1997) Expression of clusterin/sulfated glycoprotein-2 under conditions of heat stress in rat Sertoli cells and a mouse Sertoli cell line J. Androl. 18,257-263[Abstract/Free Full Text]
  31. Lewin, A. R., Reid, L. E., McMahon, M., Stark, G. R., Kerr, I. M. (1991) Molecular analysis of a human interferon-inducible gene family Eur. J. Biochem. 199,417-423[Medline]
  32. Sugihara, K., Sugihara, T., Mohandas, N., Hebbel, R. P. (1992) Thrombospondin mediates adherence of CD36+ sickle reticulocytes to endothelial cells Blood 80,2634-2642[Abstract/Free Full Text]
  33. Rader, D. J., Dugi, K. A. (2000) The endothelium and lipoproteins: insights from recent cell biology and animal studies Semin. Thromb. Hemost. 26,521-528[CrossRef][Medline]
  34. Iwamura, T., Yoneyama, M., Yamaguchi, K., Suhara, W., Mori, W., Shiota, K., Okabe, Y., Namiki, H., Fujita, T. (2001) Induction of IRF-3/-7 kinase and NF-kappaB in response to double-stranded RNA and virus infection: common and unique pathways Genes Cells 6,375-388[Abstract]
  35. Schick, B. P., Gradowski, J. F., San Antonio, J. D. (2001) Synthesis, secretion, and subcellular localization of serglycin proteoglycan in human endothelial cells Blood 97,449-458[Abstract/Free Full Text]
  36. Kulseth, M. A., Kolset, S. O., Ranheim, T. (1999) Stimulation of serglycin and CD44 mRNA expression in endothelial cells exposed to TNF-alpha and IL-1alpha Biochim. Biophys. Acta 1428,225-232[Medline]
  37. Ashkar, S., Weber, G. F., Panoutsakopoulou, V., Sanchirico, M. E., Jansson, M., Zawaideh, S., Rittling, S. R., Denhardt, D. T., Glimcher, M. J., Cantor, H. (2000) Eta-1 (osteopontin): an early component of type-1 (cell-mediated) immunity Science 287,860-864[Abstract/Free Full Text]
  38. Kumon, Y., Suehiro, T., Hashimoto, K., Sipe, J. D. (2001) Dexamethasone, but not IL-1 alone, upregulates acute-phase serum amyloid A gene expression and production by cultured human aortic smooth muscle cells Scand. J. Immunol. 53,7-12[CrossRef][Medline]
  39. Shiau, A., Chen, Y., Liao, C., Huang, Y., Wu, C. (2001) Prothymosin alpha enhances protective immune responses induced by oral DNA vaccination against pseudorabies delivered by Salmonella choleraesuis Vaccine 19,3947-3956[CrossRef][Medline]
  40. Wu, C. L., Shiau, A. L., Lin, C. S. (1997) Prothymosin alpha promotes cell proliferation in NIH3T3 cells Life Sci. 61,2091-2101[CrossRef][Medline]
  41. Van Oosten, M., Rensen, P. C., Van Amersfoort, E. S., Van Eck, M., Van Dam, A. M., Breve, J. J., Vogel, T., Panet, A., Van Berkel, T. J., Kuiper, J. (2001) Apolipoprotein E protects against bacterial lipopolysaccharide-induced lethality. A new therapeutic approach to treat gram-negative sepsis J. Biol. Chem. 276,8820-8824[Abstract/Free Full Text]
  42. Chan, J., Leenen, P. J., Bertoncello, I., Nishikawa, S. I., Hamilton, J. A. (1998) Macrophage lineage cells in inflammation: characterization by colony-stimulating factor-1 (CSF-1) receptor (c-Fms), ER-MP58, and ER-MP20 (Ly- 6C) expression Blood 92,1423-1431[Abstract/Free Full Text]
  43. Tanaka, M., Marunouchi, T., Sawada, M. (1997) Expression of Ly-6C on microglia in the developing and adult mouse brain Neurosci. Lett. 239,17-20[CrossRef][Medline]
  44. Sato, N., Yahata, T., Santa, K., Ohta, A., Ohmi, Y., Habu, S., Nishimura, T. (1996) Functional characterization of NK1.1 + Ly-6C+ cells Immunol. Lett. 54,5-9[CrossRef][Medline]
  45. Martiniello, R., Burton, R. C., Smart, Y. C. (1996) Ly-6C+ natural T (NT) cells mediate immune surveillance against NK-sensitive and NK-resistant transplantable tumours in certain strains of mice Int. J. Cancer 66,532-537[CrossRef][Medline]
  46. Blanchoin, L., Pollard, T. D., Mullins, R. D. (2000) Interactions of ADF/cofilin, Arp2/3 complex, capping protein and profilin in remodeling of branched actin filament networks Curr. Biol. 10,1273-1282[CrossRef][Medline]
  47. Green, L. J., Mould, A. P., Humphries, M. J. (1998) The integrin beta subunit Int. J. Biochem. Cell Biol. 30,179-184[CrossRef][Medline]
  48. Munz, B., Gerke, V., Gillitzer, R., Werner, S. (1997) Differential expression of the calpactin I subunits annexin II and p11 in cultured keratinocytes and during wound repair J. Invest. Dermatol. 108,307-312[CrossRef][Medline]
  49. Rocnik, E., Chow, L. H., Pickering, J. G. (2000) Heat shock protein 47 is expressed in fibrous regions of human atheroma and Is regulated by growth factors and oxidized low-density lipoprotein Circulation 101,1229-1233[Abstract/Free Full Text]
  50. Williams, R. S. (2000) Heat shock protein 47: a chaperone for the fibrous cap? Circulation 101,1227-1228[Free Full Text]
  51. Ma, X., Labinaz, M., Goldstein, J., Miller, H., Keon, W. J., Letarte, M., O’Brien, E. (2000) Endoglin is overexpressed after arterial injury and is required for transforming growth factor-beta-induced inhibition of smooth muscle cell migration Arterioscler. Thromb. Vasc. Biol. 20,2546-2552[Abstract/Free Full Text]
  52. Rodriguez-Barbero, A., Obreo, J., Eleno, N., Rodriguez-Pena, A., Duwel, A., Jerkic, M., Sanchez-Rodriguez, A., Bernabeu, C., Lopez-Novoa, J. M. (2001) Endoglin expression in human and rat mesangial cells and its upregulation by TGF-beta1 Biochem. Biophys. Res. Commun. 282,142-147[CrossRef][Medline]
  53. Wang, A. C., Fu, L. (2001) Fibronectin regulates the activation of THP-1 cells by TGF-beta1 Inflamm. Res. 50,142-148[CrossRef][Medline]
  54. Gratchev, A., Guillot, P., Hakiy, N., Politz, O., Orfanos, C. E., Schledzewski, K., Goerdt, S. (2001) Alternatively activated macrophages differentially express fibronectin and its splice variants and the extracellular matrix protein betaIG-H3 Scand. J. Immunol. 53,386-392[CrossRef][Medline]
  55. Kester, H. A., Blanchetot, C., den Hertog, J., van der Saag, P. T., van der Burg, B. (1999) Transforming growth factor-beta-stimulated clone-22 is a member of a family of leucine zipper proteins that can homo- and heterodimerize and has transcriptional repressor activity J. Biol. Chem. 274,27439-27447[Abstract/Free Full Text]



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