CHEST
Original Research
LUNG TRANSPLANTATION
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
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.
(CHEST 2006; 130:847– 854)
Key words: BAL; graft rejection; lung transplantation; microarray analysis
Abbreviations: BOS ⫽ bronchiolitis obliterans syndrome; FGF ⫽ fibroblast growth factor; IL ⫽ interleukin;
ISHLT ⫽ International Society of Heart and Lung Transplantation; OB ⫽ obliterative bronchiolitis; PDGF ⫽ plateletderived growth factor; TNF ⫽ tumor necrosis factor
transplantation is the only viable treatment
L ung
option for many end-stage lung diseases. De1
spite an improved 1-year survival rate of 73%, the
*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.
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.
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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
Manuscript received September 6, 2005; revision accepted February 28, 2006.
Reproduction of this article is prohibited without written permission
from the American College of Chest Physicians (www.chestjournal.
org/misc/reprints.shtml).
Correspondence to: Sangeeta M. Bhorade, MD, FCCP, University of Chicago Hospitals, 5841 S Maryland Ave, MC 6076,
Chicago, IL 60637; e-mail: [email protected]
DOI: 10.1378/chest.130.3.847
CHEST / 130 / 3 / SEPTEMBER, 2006
847
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.5–7 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
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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 ␤-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 ␮L 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 phosphataseconjugated 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).
Original Research
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 1 and
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.
Table 1—Demographic Data of BOS and Control
Patients*
Variables
Age, yr
Male gender
Time from transplant, d
Diagnosis
COPD
Cystic fibrosis
Idiopathic pulmonary fibrosis
Other
BOS stage†
0-p
I
II
BOS
Patients
(n ⫽ 11)
Control
Subjects
(n ⫽ 9)
p Value
50 ⫾ 9
8 (73)
397 ⫾ 426
50 ⫾ 11
3 (33)
271 ⫾ 78
0.84
0.17
0.31
7 (64)
1 (9)
1 (9)
2 (18)
4 (44)
1 (11)
2 (22)
2 (22)
0.65
1.0
0.56
1.0
2 (18)
6 (55)
3 (27)
*Values are given as the mean ⫾ SD or No. (%), unless otherwise
indicated. Other ⫽ ␣ 1-antitrypsin deficiency (BOS patients), eosinophilic granuloma (BOS patients), sarcoidosis (control subjects), and
lymphangioleiomyomatosis (control subjects).
†Stage 0-p ⫽ FEV1 81 to 90% of baseline and/or forced expiratory
flow, midexpiratory phase ⱕ 75% of baseline; stage 1 ⫽ FEV1 66 to
80% of baseline; stage 2 ⫽ FEV1 51 to 65% of baseline.
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Table 2—Immunosuppression Regimens of BOS and
Control Patients*
Variables
BOS
Control
p Value
Pred/Aza/Tac
Pred/Sir/Tac
Other
73 (8)
9 (1)
18 (2)
78 (7)
11 (1)
11 (1)
1.0
1.0
1.0
*Values are given as % (No.), unless otherwise indicated.
Pred ⫽ prednisone; Aza ⫽ azathioprine; Tac ⫽ tacrolimus; Sir ⫽
sirolimus.
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).
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.
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
Table 3—BAL Fluid Cell Differentials of BOS and
Control Patients
Variables
BOS Patients
Control Subjects
p Value
Cell count, cells/␮L
Macrophages, %
Neutrophils, %
Lymphocytes, %
Other, %
152
58.9
13.3
15.6
12.2
135
89.8
2.3
7.5
0.4
0.69
0.02
0.12
0.36
0.08
CHEST / 130 / 3 / SEPTEMBER, 2006
849
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.
had her FEV1 return to 80% of baseline, and one
patient had mild BOS (ie, FEV1, 75% of baseline
value).
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
Table 4 —Genes Significantly Differentially Expressed Between BOS and Control Patients*
Gene Name
IL-1␤
CD40 ligand
TNF
Ligand, superfamily, member 11
Ligand, superfamily, member 9
Superfamily, member 2
IL-2
Fas ligand (TNF superfamily, member 6)
TNF (ligand) superfamily, member 7
Lymphotoxin ␣ (TNF superfamily, member 1)
Ribosomal protein L13a
PDGF-BB
VEGF
Bone morphogenetic protein 6
FGF-2 (basic)
FGF-5
␤-actin§
Gene Symbol
Genebank
Fold Change
q Value,† %
Pathway
IL1B‡
CD40LG‡
TNFSF11
M15330
L07414
NM_003701
2.92
2.57
2.17
5.01
5.01
6.44
I
A
A, I
TNFSF9
TNF‡
IL2‡
FASLG
TNFSF7
LTA
RPL13A
PDGF b‡
VEGF
BMP6
FGF2‡
FGF5
ACTB
U03398
X01394
U25676
U08137
L08096
D12614
NM_012423
X63966
M32977
NM_001718
NM_002006
NM_004464
X00355
2.04
1.92
1.90
1.88
1.88
1.86
1.80
1.54
1.53
0.62
0.38
0.33
1.04
5.01
5.01
5.01
6.44
6.44
6.44
6.44
5.01
10.61
8.46
5.01
8.46
84.7
I
A, I
A, I
A
I
A, I
O
F
F
O
F
F
O
*Fold change ⫽ the ratio of mean signal intensities (mean signal of BOS patients/mean signal of control subjects), expressed in arithmetic means;
A ⫽ apoptotic; F ⫽ fibrotic; I ⫽ inflammatory; O ⫽ other; VEGF ⫽ vascular endothelial growth factor.
†q Value is similar to p value but is adapted to the analysis of a large number of genes.
‡Genes that have previously been identified as being involved in chronic rejection.18,19,32–34
§Shown as a negative control.
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Original Research
Table 5—Sequential Spirometry Values in Seven
Patients With BOS*
Variables
Pre-BOS
BOS
Ongoing BOS
Time from transplant, d
Average FEV1 ratio, %
168 ⫾ 100
96 ⫾ 6.6†
253 ⫾ 143
66 ⫾ 5.7
271 ⫾ 71
71 ⫾ 7.3
*Values are given as the mean ⫾ SD. FEV1 ratio ⫽ FEV1 divided by
the baseline FEV1 (ie, the average of the two best posttransplant
measurements, taken at least 3 weeks apart).
†p ⬍ 0.001 between Pre-BOS and BOS (by one-way analysis of
variance).
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.
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
Table 6 —BAL Fluid Cell Differentials of Longitudinal
Samples*
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.
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Variables
Pre-BOS
BOS
Ongoing BOS
Cell count, cells/␮L
Macrophages, %
Neutrophils, %
Lymphocytes, %
Other, %
622
81
11
6
2
152
59
13
16
12
1,090
62
23
12
3
*p ⫽ 0.25 for time by cell type analysis (by repeated measures
analysis of variance).
CHEST / 130 / 3 / SEPTEMBER, 2006
851
Figure 3. Sequential expression of CD40LG, Fas ligand, and PDGF-␤. 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.
various disease processes including malignancy, renal transplantation, and cardiopulmonary diseases.10 –14 Gimino et al15 identified several candidate
genes that may be involved in the acute rejection of
the lung allograft. They concluded that these pat852
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terns 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 pathOriginal Research
ways in the pathogenesis of BOS. Previous studies16 –24
have shown isolated up-regulation of individual inflammatory, apoptotic, or fibrotic mediators (plateletderived growth factor [PDGF], fibroblast growth factor
[FGF], tumor necrosis factor [TNF]-␣, and insulin-like
growth factor-1), leading to the speculation that BOS
often occurs as a result of an initial insult (alloimmunedependent 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 studies18,25–31 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-␣ (apoptotic pathway), PDGF-␤, 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
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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-1␤ and TNF-␣ (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
CHEST / 130 / 3 / SEPTEMBER, 2006
853
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.
19
20
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