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(Chest. 2006;130:847-854.)
© 2006 American College of Chest Physicians

Sequential Gene Expression Profiling in Lung Transplant Recipients With Chronic Rejection*

Brandon S. Lu, MD; Andrew D. Yu, MD; Xiaofeng Zhu, MD; Edward R. Garrity, Jr, MD, FCCP; Wickii T. Vigneswaran, MD, FCCP and Sangeeta M. Bhorade, MD, FCCP

* From the Department of Neurology (Dr. Lu), Northwestern University, Feinberg School of Medicine, Chicago, IL; the Department of Pulmonary, Critical Care, and Sleep Medicine (Dr. Yu), DuPage Medical Group, Lombard, IL; the Department of Preventive Medicine and Epidemiology (Dr. Zhu), Loyola University Medical Center, Maywood, IL; the Department of Medicine (Drs. Garrity and Bhorade), Section of Pulmonary and Critical Care, and the Department of Cardiac and Thoracic Surgery (Dr. Vigneswaran), University of Chicago, Chicago, IL.

Correspondence to: Sangeeta M. Bhorade, MD, FCCP, University of Chicago Hospitals, 5841 S Maryland Ave, MC 6076, Chicago, IL 60637; e-mail: sbhorade{at}medicine.bsd.uchicago.edu

Abstract

Study objectives: Chronic allograft rejection is the leading cause of morbidity and mortality for long-term survivors of lung transplantation. Previous studies have implicated only isolated genes in the development of chronic rejection and have not examined multiple pathways in an individual concurrently. Using microarray technology, we identified and compared gene expression profiling in lung transplant recipients with and without chronic rejection, and follow sequential expression of genes differentially expressed between the two groups.

Design: Prospective, cohort study.

Setting: Single lung transplant center.

Patients or participants: Eleven transplant recipients with chronic rejection were matched with 9 control transplant recipients.

Interventions: All recipients underwent surveillance bronchoscopies at predetermined times to rule out infection and/or acute rejection. Gene expression profiling was obtained from hybridizing BAL fluid cell RNA to a 96-gene microarray.

Measurements and results: Fifteen genes were found to be significantly differentially expressed between the two patient groups, and they are involved in inflammatory, fibrotic, and apoptotic pathways. Temporal expression of the significant genes demonstrated a change in their levels at the onset of chronic rejection, with normalization to prerejection levels as rejection continued.

Conclusions: We conclude that microarray technology is valuable in studying the mechanism of chronic lung rejection, and the expression of genes in multiple pathways is elevated in patients with chronic lung rejection.

Key Words: BAL • graft rejection • lung transplantation • microarray analysis

Lung transplantation is the only viable treatment option for many end-stage lung diseases.1 Despite an improved 1-year survival rate of 73%, the 5-year survival rate remains poor at 45%.2 Chronic allograft rejection, manifested histopathologically as obliterative bronchiolitis (OB), is the single largest contributor to morbidity and mortality after 1 year, affecting up to 50 to 60% of patients who survive to 5 years after undergoing transplantation.3 The histology of OB consists of dense eosinophilic hyaline fibrous plaques in the submucosa of the small airways, which result in partial or complete luminal compromise.4 Bronchiolitis obliterans syndrome (BOS), the clinical surrogate of OB, describes deterioration in lung function measured by spirometry as defined by the International Society of Heart and Lung Transplantation (ISHLT).

Unfortunately, the pathogenesis underlying the development of BOS has not been well elucidated. It has been speculated that several alloimmune-dependent and alloimmune-independent mechanisms are implicated in the development of BOS. Recent data have suggested that the pathogenesis of BOS may be the result of interactions between several coexisting biological processes.567 Although individual studies have found single elevations of various inflammatory, fibrotic mediators and markers of epithelial cell apoptosis, previous technology has not enabled investigators to evaluate several processes simultaneously in a given individual.

Gene microarray technology provides the ability to profile the expression pattern of multiple pathways simultaneously in a given individual. While prior investigations have identified individual markers of BOS, they are limited by the small numbers of genes studied and the isolation of these genes that are involved in the pathogenesis of BOS. In addition, they are also less likely to identify novel genes that are not already known or expected to be associated with BOS.

We report the results of gene microarray analysis applied to bronchoalveolar cells from lung transplant recipients with and without BOS. We hypothesize that the expression levels of genes involved in multiple pathways including fibrosis, inflammation, and apoptosis are elevated simultaneously in BOS.

Materials and Methods

Patient Selection and Sample Collection
Patients who underwent lung transplantation at Loyola University Medical Center between August 1999 and August 2003 were eligible for the study. Eleven patients in whom BOS, as defined by the ISHLT, developed were identified and included in this study. The ISHLT defines BOS based on a decrease in FEV1 of at least 10% from baseline, in the absence of documented infection, acute rejection, or airway stenosis. The baseline value is the average of the two best posttransplant FEV1 measurements made at least 3 weeks apart. Nine control lung transplant recipients who were matched for age, time from transplantation, and underlying diagnosis were included for comparison. The cohort was not gender matched. Two of the nine control patients had two distinct BAL fluid samples that were matched to two separate BOS patients. This study was approved by the institutional review board at Loyola University Medical Center, and written informed consent was obtained from all study patients.

Bronchoscopies in control patients, the results of which were used for comparisons, were performed at 1, 3, 6, 9, and 12 months after transplantation for surveillance. The initial bronchoscopy (pre-BOS) for patients in whom BOS eventually developed was performed for routine surveillance during the first posttransplant year. Patients in whom BOS developed had bronchoscopy performed at the time of BOS diagnosis in order to exclude infection or acute rejection. Infection was diagnosed if the BAL fluid culture showed any bacteria, fungus, or virus, or if the serum cytomegalovirus digene was detectable. A pathologist reviewed all the results of transbronchial biopsies, and acute rejection was graded with an A and B score according to ISHLT criteria. There was no evidence of infection or acute rejection (A or B, respectively) in any of the BAL fluid samples evaluated in this study. For longitudinal analysis, available samples immediately preceding and following the diagnosis of BOS were used.

Bronchoscopy was performed transorally, and BAL fluid and transbronchial biopsy samples were taken from the middle and lower lobes, respectively, of the transplanted lung or the right lung if the patient had bilateral transplantation. Approximately 25 mL of BAL fluid was chilled on ice for microarray analysis, and the remaining fluid was sent for bacterial, viral, and fungal cultures. Five transbronchial biopsy sample were taken from the lower lobe and were sent to the pathology department for a histologic assessment of acute rejection. Cells were separated by centrifugation, and cell counts were determined by a hemocytometer. All culture and histologic analyses were conducted by personnel who were blinded to the results of the study.

Sample Processing and Microarray
BAL fluid was centrifuged at 300g and 4°C for 10 min to separate cells from the supernatant. Cells were then washed with 20 mL of Hank balanced salt solution (Mediatech, Inc; Herndon, VA) by three successive centrifugations and were sheared with a 21-gauge needle. The sheared cells were stored in guanidium thiocyanate and ß-mercaptoethanol at –80°C.

Total RNA was isolated (RNeasy Mini Kit; Qiagen; Valencia, CA) according to the manufacturer’s directions. Briefly, one volume of 70% ethanol was added to the sheared cell sample and applied to a mini spin column (Rneasy; Qiagen). The column was centrifuged then washed; RNA was eluted with 30 µL of RNase-free water. The quality and quantity of RNA was determined by measuring the absorbance at 260 nm (A260) and 280 nm (A280). RNA quality was checked by gel electrophoresis.

We synthesized complementary DNA probes using a commercial kit (Nonradioactive AmpoLabeling-LPR Kit; SuperArray; Frederick, MD). First-strand complementary DNA was generated using total RNA and reverse transcriptase. After hydrolyzing the RNA, complementary DNA was amplified and labeled with DNA polymerase and biotin-16-deoxyuridine triphosphate in a thermal cycler for 30 cycles. The complementary DNA was then denatured by heat and hybridized to an oligonucleotide microarray (GEArray Q series; SuperArray) at 60°C overnight with constant rotation. The commercial microarray contained 96 genes that encode common cytokines involved in numerous metabolic pathways. After discarding the complementary DNA probes, the array was first incubated with alkaline phosphatase-conjugated streptavidin, then with a chemiluminescent substrate. We then exposed the array to radiograph film and captured the image with a charge-coupled device camera. The signals were converted into numerical data using software (ScanAlyze; SuperArray).

Statistical Analysis
Demographic data were compared between patients with and without BOS by Student t test for continuous variables and by Fisher exact test for categoric variables. All errors are reported as SDs. Longitudinal analysis was performed with one-way analysis of variance. Individual gene signal value, or gene expression, was normalized to the median value of each array (ie, normalized expression). To select genes with significant differentiate expression between the two groups we used a statistical software for microarrays (Significance Analysis of Microarrays; Stanford University Labs; Stanford, CA).8 Significance was defined by the following two criteria: exceeding a predetermined fold change; and a tuning parameter delta. A fold change of 1.5 was required to be significant. Specifically, genes in the BOS groups must be differentially expressed by 50% compared to the control group. A delta value of 0.45 was used to give a false-positive rate of 10% (ie, 1 to 2 of 15 genes are false-positive findings). The relationship of the genetic expression pattern between the two groups was determined with two-dimensional hierarchical clustering analysis and viewed (TreeView; Stanford University; Stanford, CA).9

Results

Patients and Samples
Demographic and immunosuppression data for both groups of patients are presented in Tables 1and 2 , respectively. The majority of patients with BOS had stage 1 BOS. The most common immunosuppressive regimen included tacrolimus/azathioprine and prednisone. To ensure that samples were appropriately matched, two of the control patients had samples from two separate time points that were used for analysis. BAL fluid cell counts and differential cell counts are displayed in Table 3 .


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  		Table 1. Demographic Data of BOS and Control Patients*

 

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  		Table 2. Immunosuppression Regimens of BOS and Control Patients*

 

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  		Table 3. BAL Fluid Cell Differentials of BOS and Control Patients

 
Microarray Analysis
To verify the reproducibility of our results, we divided a sample in two and processed each half separately for hybridization with two microarrays. The two arrays showed significant correlation (correlation coefficient, 0.96), thus validating the reproducibility of our results (Fig 1 ).


Figure 1
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  		Figure 1. Reproducibility of the microarray. One sample was divided into two halves and processed in parallel for hybridization with separate arrays. The expression of individual genes normalized to the median value of each array is shown.

 
Using software (Significance Analysis of Microarrays), we found 15 genes that were significantly differentially expressed between the two groups of patients (Table 4 ). Genes representing inflammatory, fibrotic, and apoptotic pathways were significantly overexpressed in patients with BOS compared to the control patients.


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  		Table 4. Genes Significantly Differentially Expressed Between BOS and Control Patients*

 
Clustering
A two-dimensional hierarchical clustering program (Cluster; Stanford University) organized the data based on similarities. The results are displayed using another program (TreeView) [Fig 2 ]. Most patients with BOS were grouped together, signifying similar expression patterns when compared to patients without BOS. However, there was some evidence of relative molecular heterogeneity in both cluster groups. Of the three control patients who clustered with the BOS group, lymphoma developed in one patient and died soon thereafter, one patient was lost to follow-up, and the third patient is alive and well without BOS. Of the three BOS patients who clustered with the control group, one patient had his FEV1 return to baseline values, one patient had her FEV1 return to 80% of baseline, and one patient had mild BOS (ie, FEV1, 75% of baseline value).


Figure 2
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  		Figure 2. Two-dimensional hierarchical clustering analysis of logarithmic gene expression patterns of BAL fluid from BOS and control patients. The clustering algorithm approximately separated the samples from BOS and control patients into two groups. Red square = gene expression of > 1; green square = gene expression of < 1; black square = gene expression of 1.

 
Longitudinal Analysis
We analyzed sequential BAL fluid samples obtained from BOS patients to identify temporal expression of the significant genes. Samples acquired at bronchoscopy immediately before and after the diagnosis of BOS was made were processed and hybridized to microarrays. Seven patients had sequential samples. Table 5 shows the spirometry value and time from transplant of the samples obtained at three different time points. Table 6 shows the sequential cell count and cell type in those seven patients. We performed additional bronchoscopy on patients with BOS when clinically indicated, as defined by worsening respiratory symptoms. Seven of the BOS patients underwent clinically indicated bronchoscopies after the initial diagnosis of BOS. Longitudinal gene expression from three cellular pathways is shown in Figure 3 . Two of seven patients with sequential samples had a change in therapy from azathioprine or mycophenolate mofetil to rapamycin. Another patient was treated with phototherapy.


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  		Table 5. Sequential Spirometry Values in Seven Patients With BOS*

 

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  		Table 6. BAL Fluid Cell Differentials of Longitudinal Samples*

 

Figure 3
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  		Figure 3. Sequential expression of CD40LG, Fas ligand, and PDGF-ß. Gene expression was normalized to the median value of each array. Consecutive samples were obtained from seven BOS patients. Expression was given on an arithmetic scale.

 
Discussion

In this study, we identified 15 candidate genes that were differentially expressed in lung transplant recipients with BOS compared to those patients without BOS. Specifically, genes from three distinct pathways, including inflammatory, apoptotic, and fibrotic pathways, were up-regulated simultaneously in lung transplant recipients with BOS. In addition, we showed the sequential patterns of up-regulation and normalization of gene expression for these 15 genes during the initial diagnosis and progression of BOS, respectively.

To our knowledge, this is the first report of gene expression profiling in lung transplant recipients with BOS. Gene expression profiling, which is a rapidly growing technology that is able to monitor gene expression levels (up to tens of thousands), has been used extensively in the past few years to provide a more comprehensive understanding of various disease processes including malignancy, renal transplantation, and cardiopulmonary diseases.1011121314 Gimino et al15 identified several candidate genes that may be involved in the acute rejection of the lung allograft. They concluded that these patterns of gene expression may represent biomarkers that may assist with the prediction, diagnosis, and treatment of acute lung allograft rejection.15

Likewise, this technology enabled us to detect overexpressed genes from three different biological pathways in the pathogenesis of BOS. Previous studies161718192021222324 have shown isolated up-regulation of individual inflammatory, apoptotic, or fibrotic mediators (platelet-derived growth factor [PDGF], fibroblast growth factor [FGF], tumor necrosis factor [TNF]-{alpha}, and insulin-like growth factor-1), leading to the speculation that BOS often occurs as a result of an initial insult (alloimmune-dependent or alloimmune-independent) that leads to repetitive endothelial or epithelial injury followed by inflammation and repair by the proliferation of smooth muscle cells, mesenchymal cells, and fibroblasts. However, none of these prior studies1825262728293031 have evaluated the presence of several mediators simultaneously. In contrast, we detected the simultaneous up-regulation of interleukin (IL)-1, IL-2 (inflammatory pathway), Fas ligand, TNF-{alpha} (apoptotic pathway), PDGF-ß, and vascular endothelial growth factor (fibrotic pathway), suggesting that there is extensive crosstalk among the different pathways in the development of BOS. We also identified genes that were involved in other pathways that may prove to have important roles in the development of BOS. In addition to supporting our current knowledge of the pathogenesis of BOS, these data provide a more comprehensive view of the development of BOS. Interestingly, our gene arrays in patients with BOS did not show gene expression patterns that were similar to those in lung transplant recipients with acute rejection, as had been presented by Gimino and colleagues.15 However, we did not find this to be surprising given the different proposed mechanisms of the processes that occur in acute and chronic rejection.

In addition, sequential expression data of the significant genes (Fig 3) showed that expression levels peak at the onset of BOS and normalize to pre-BOS levels with ongoing BOS, compared to stable gene expression in transplant recipients without BOS. Importantly, these data show the simultaneous up-regulation of genes from the inflammatory, apoptotic, and fibrotic pathways, supporting the interaction of these various pathways at the onset of BOS. Because our study was designed to specifically look at BAL fluid at the onset of BOS, we were not able to identify predictive biomarkers prior to the onset of BOS. Further investigations using a prospective, longitudinal study design may be able to use this technology to identify molecular biomarkers in the BAL fluid of individuals in whom BOS eventually develop.

We were able to differentiate lung transplant recipients with BOS from those without BOS by using hierarchical cluster analysis. However, there was significant molecular heterogeneity within the BOS cluster. Similarly, Sarwal and colleagues12 showed previously unrecognized molecular heterogeneity in renal transplant recipients in whom acute renal allograft rejection developed. They identified three distinct subtypes of acute rejection that were associated with differences in immunologic and cellular features and clinical course. In contrast, we did not identify any specific characteristic gene expression patterns that distinguished subsets of patients within the BOS group. However, given the variable clinical patterns of BOS, the different immunosuppressive regimens and the varying stages of BOS in our patients, it is not surprising that there was significant heterogeneity within the gene expression cluster of patients in whom BOS eventually developed. Certainly, a prospective study with a larger sample size of patients would be needed to draw conclusions regarding gene expression signature profiles in subsets of patients with BOS.

The potential limitations of this study include the relative heterogeneity of the patients in whom BOS eventually developed and the small sample size. We specifically chose patients who did not have infection or acute rejection at the time of bronchoscopy, which significantly limited our sample size. In addition, we were unable to match for gender given the priority of matching for age, diagnosis, and time from transplantation. Although gender matching would have been ideal, the importance of gender in BOS development is unknown at this time. Another confounding factor is the difference in BAL fluid cell composition between BOS and control patients, which could potentially account for some of the differential expression pattern between the two groups. Although we realize this limitation, the overexpression of IL-1ß and TNF-{alpha} (both produced predominantly by activated macrophages) in BOS patients with a lower percentage of macrophages in BAL fluid suggests that the expression of these genes is truly up-regulated. In addition, since gene microarray analysis is a new technology, the standard methods of statistical analysis and analysis of significance has not been firmly established. We used a fold change of 1.5 as significant, and a delta value of 0.45 was used to give a false positive rate of 10% (ie, 1 to 2 of 15 genes are false-positive results), as is currently recommended in the literature.8 A false-positive rate of 10% gave an acceptable percentage of false significant genes compared to genes defined as significant.

In conclusion, BOS remains the main obstacle to long-term survival after lung transplantation. The major impediments to impacting this disease process include the inability to identify appropriate predictors prior to the development of BOS and the incomplete, fragmentary knowledge of the underlying pathogenesis of BOS. As a result, current therapies are often initiated after the lung fibrosis has become irreversible and may not target appropriate pathways of pathogenesis. Gene microarray technology enabled us to detect 15 differentially expressed genes in lung transplant recipients with BOS compared to those without BOS. In this way, gene expression profiling provides a global, comprehensive understanding of the pathogenesis of BOS and outlines independent pathways that future therapies must target.

Footnotes

Abbreviations: BOS = bronchiolitis obliterans syndrome; FGF = fibroblast growth factor; IL = interleukin; ISHLT = International Society of Heart and Lung Transplantation; OB = obliterative bronchiolitis; PDGF = platelet-derived growth factor; TNF = tumor necrosis factor

The authors have reported to the ACCP that no significant conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Received for publication September 6, 2005. Accepted for publication February 28, 2006.

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