Proteomic and Computational Analysis of
Bronchoalveolar Proteins during the Course of the
Acute Respiratory Distress Syndrome
Dong W. Chang1,2*, Shinichi Hayashi1,2*, Sina A. Gharib2,5,6*, Tomas Vaisar3, S. Trevor King4, Mitsuhiro Tsuchiya4,
John T. Ruzinski2, David R. Park2, Gustavo Matute-Bello1,2, Mark M. Wurfel2, Roger Bumgarner4,
Jay W. Heinecke3, and Thomas R. Martin1,2
1Medical Research Service of the VA Puget Sound Healthcare System, Seattle, Washington; Divisions of2Pulmonary and Critical Care Medicine and
3Endocrinology and Metabolism, Department of Medicine; and4Department of Microbiology;5Center for Lung Biology; and6Fred Hutchinson
Cancer Research Institute, University of Washington, Seattle, Washington
Rationale: Acute lung injury causes complex changes in protein ex-
pression in the lungs. Whereas most prior studies focused on single
proteins, newer methods allowing the simultaneous study of many
proteins could lead to a better understanding of pathogenesis and
new targets for treatment.
tein expression in the bronchoalveolar lavage fluid (BALF) of patients
during the course of the acute respiratory distress syndrome (ARDS).
Methods: Using two-dimensional difference gel electrophoresis (DIGE),
volunteers (n 5 9). Thepatterns of protein expressionwere analyzed
using principal component analysis (PCA). Biological processes that
were enriched in the BALF proteins of patients with ARDS were
identified using Gene Ontology (GO) analysis. Protein networks that
model the protein interactions in the BALF were generated using
Ingenuity Pathway Analysis.
Measurements and Main Results: An average of 991 protein spots were
detected using DIGE. Of these, 80 protein spots, representing 37
unique proteins in all of the fluids, were identified using mass spec-
trometry. PCA confirmed important differences between the proteins
in the ARDS and normal samples. GO analysis showed that these
differences are due to the enrichment of proteins involved in inflam-
mation, infection, and injury. The protein network analysis showed
revealed unexpected central components in the protein networks.
Conclusions: Proteomics and protein network analysis reveals the
complex nature of lung protein interactions in ARDS. The results
provide new insights about protein networks in injured lungs, and
identify novel mediators that are likely to be involved in the patho-
genesis and progression of acute lung injury.
Keywords: acute respiratory distress syndrome; acute lung injury;
proteomic analysis; bronchoalveolar lavage; 2D gel electrophoresis
The study of single pathways and one-dimensional protein–
(ALI) and the acute respiratory distress syndrome (ARDS),
places major limitations on understanding the pathophysiology
of these complex diseases. Previous studies suggest that numer-
ous biological processes, including inflammation, apoptosis, and
thrombosis, are involved in the pathogenesis of ALI and ARDS
(1). Traditional research methods that explore single biological
processes. In contrast, systems-based methodologies, such as
proteomics, analyze global biological changes and provide an
opportunity to examine the complexity that is inherent in human
diseases, such as ALI and ARDS.
Proteomics methods have been applied to the study of lung
injury by several groups of investigators. Bowler and coworkers
used an electrophoresis-based proteomics method to show that
patients with ARDS have differences in both the expression and
post-translational modification of proteins in distal lung fluid as
compared with healthy control subjects (2). Schnapp and col-
leagues used a shotgun proteomics approach to identify insulin-
like growth factor-binding protein-3 (IGFBP-3) as a novel medi-
ator of apoptotic pathways in acute lung injury (3). De Torre and
coworkers identified markers of lung inflammation, such as S100
A8 and A9 proteins, in the BALF of subjects challenged with
TOF and electrophoresis-based proteomics methods (4). These
and dynamic changes that occur during the course of lung injury.
profile the changes in protein expression in the lungs at the onset
and during the course of acute lung injury to examine the protein
pathways that have important roles in its pathogenesis. We used
a quantitative proteomics approach to profile proteins in the
bronchoalveolar lavage (BAL) fluid (BALF) of patients with
ARDS at Days 1, 3, and 7 after the onset of illness and compared
the results with protein profiles in the BALF of healthy control
AT A GLANCE COMMENTARY
Scientific Knowledge on the Subject
Single inflammatory pathways do not completely account
for the onset, perpetuation, or resolution of lung injury.
New approaches accounting for the complexity of the
inflammatory response and changes that occur during the
course of acute lung injury are needed.
What This Study Adds to the Field
Proteomic and computational network analysis of proteins
in injured lungs shows new relationships among proteins
and identifies new groups of mediators that could be
targets for novel treatments.
(Received in original form December 26, 2007; accepted in final form July 21, 2008)
*These authors contributed equally to this study.
Funding: NIH/NHLBI HL073996, NIH/NHBLI HL090298.
Correspondence and requests for reprints should be addressed to Thomas R.
Martin, M.D., Pulmonary Research Laboratories, VA Puget Sound Health Care
System, 1660 S. Columbian Way, 151L Seattle, WA 98108. E-mail: trmartin@u.
This article has an online supplement, which is accessible from this issue’s table of
contents at www.atsjournals.org.
Am J Respir Crit Care Med
Originally Published in Press as DOI: 10.1164/rccm.200712-1895OC on July 24, 2008
Internet address: www.atsjournals.org
Vol 178. pp 701–709, 2008
subjects. We then applied advanced methods in computational
This approach to protein network analysis identified novel medi-
atorsofacutelung injury,and showed thatproteinpathwayswere
redundant and involved in multiple biological processes. These
characteristics of the protein interactions in the lungs of patients
with ARDS have important implications for the development of
new molecular-based therapies. Some of the results of this study
have been previously reported in the form of an abstract (5).
Patients with ARDS, as defined by the American-European Consensus
Conference, were enrolled at Harborview Medical Center (Seattle,
WA), a tertiary, university-based hospital (6). The patients underwent
fiberoptic bronchoscopy and BAL in either the right middle lobe or
lingula on Days 1, 3, and 7, as described (7). The control BAL fluid
samples were obtained from healthy, nonsmoking volunteers between
the ages of 18 and 50. The experimental protocol was approved by the
Institutional Review Board of the University of Washington. Informed
consent was obtained from the patient or a legal representative.
The BALF was processed by centrifugation as described (7). For
proteomic analysis, the BALF samples were concentrated by centrifu-
gation to equivalent starting volumes (z 500 ml) using a 5-kD molecular
weight filter (Amicon Ultra-15; Millipore, Billerica, MA). The samples
were then spun through a 0.22-mm filter to remove mucus and other
insoluble products. The total protein concentrations were determined
using the BCA Protein Assay (Pierce, Rockford, IL).
analysis, all samples were depleted of six highly abundant serum proteins
(albumin, transferrin, haptoglobin, antitrypsin, IgG, IgA) using a mono-
clonal IgG immunoaffinity HPLC column (Multiple Affinity Removal
System; Agilent Technologies, Wilmington, DE). The BAL fluids were
passed over the depletion column, which absorbed the high abundance
to approximately 100 ml using a 5-kD centrifugation filter (Amicon Ulta-
15; Millipore). The final protein concentration was measured using the
2D-Quant Assay, which allows protein measurements in urea-based
solutions (Amersham Biosciences, Piscataway, NJ). The specificity, re-
producibility, and improvements in protein spot detection during proteo-
mic analysis of BALF using this approach have been reported (8, 9).
Two-Dimensional Difference Gel Electrophoresis
Proteomic analysis of the BALF samples was performed using two-
dimensional difference gel electrophoresis (DIGE; GE Healthcare,
Piscataway, NJ). In DIGE, fluorescent dyes with unique absorbance
and emission spectra (Cy3, Cy5) were used to differentially label
a reference standard (Cy3) and each experimental sample (Cy5). The
experimental and normal samples. Thus, the reference standard theo-
retically contained every protein spot that was detected in the experi-
ment. The standard and experimental samples were labeled separately
were separated first by their isoelectric points (first dimension) and then
by molecular weights (second dimension). The gel was first scanned at
a wavelength that was optimized to identify the proteins labeled with
Cy3, then rescanned at a second wavelength that was optimized to
identify proteins labeled with Cy5. The acquired images were then
superimposed and the abundances of the protein spots in the experi-
mental sample was expressed as a ratio between the spot of interest
(labeled with Cy3). Because the abundance of each protein spot was
expressed as a ratio to a common reference standard, the influence of
experimental factors that can confound the accurate measurement of
(10). The sensitivity and reproducibility of the DIGE method for
detecting changes in protein abundance in complex biological samples
have been reported (10).
For each subject, 75 mg of reference standard and 75 mg of BAL
then applied onto a single rehydrated immobilized pH gradient (IPG)
strip pH 4–7, 24 cm (GE Healthcare). Two-dimensional electrophoresis
was performed as described in the online supplement, separating
proteins by isoelectric point in the first dimension and molecular weight
in the second dimension. To identify individual protein spots, the gels
were scanned using the Typhoon 9400 Series Variable Imager (GE
Healthcare) with excitation wavelengths of 532 nm for Cy3 and 580 nm
for Cy5. This procedure was performed using the samples from each of
9 control subjects, yielding a total of 29 separate gels.
Protein Spot Analysis
The protein spots were analyzed using the Decyder software program
version 5.01 (GE Healthcare). The difference in-gel analysis (DIA)
module of the Decyder program was used to measure the abundance of
each of the protein spots in the experimental samples as a log2ratio
between the spot of interest in the experimental sample (Cy 5 wave-
length) and the corresponding spot in the reference standard (Cy3
wavelength). Next, the biological variance analysis (BVA) module of
the Decyder program was used to compare the expression of protein
than 50% of the gels were included in the analysis.
Identification of Protein Spots
For protein identification, a ‘‘picking gel’’ containing 500 mg of protein
from the pooled standard was generated using the same electrophoresis
protocol as described above, and stained after the second dimension
electrophoresis with SyproRuby (PerkinElmer, Waltham, MA). The
(Ettan DIGE Spot-picker; Amersham) with a 2-mm picking head into
a 96-well ZipPlateC18 (Millipore) for in-gel digestion and matrix-
assisted laser desorption (MALDI) mass spectrometric analysis. Each
gel spot containing a protein of interest was washed, dehydrated, and
peptides were eluted from the ZipTip of the plate in 80% acetonitrile
containing 0.1% trifluoroacetic acid (TFA), spotted on the MALDI
target (0.5 ml), dried, and overlaid with 0.5 ml of a-cyano-4-hydroxycin-
MALDI-TOF/TOF mass spectrometry. The MALDI-TOF MS and MS/
MS spectra were acquired on the ABI 4700Proteomic analyzerMALDI
tandem time-of-flight mass spectrometer with air as the collision gas
(Applied Biosystems, Foster City, CA). The MS and MS/MS spectra
were searched in combination against the human SwissProt protein
database using the MASCOT search engine (v2.0). Only protein
identifications with a confidence interval of greater than 95% based on
MASCOT MOWSE were included in the analysis.
The concentrations of IL-1b and IL-6 were measured in the BALF from
Systems, Minneapolis,MN). The concentrationof TNF-a was measured
using an ultra-sensitive ELISA kit according to the manufacturer’s
protocol (R&D Systems).
Significance testing was performed using Student’s t test, one-way
ANOVA, and Pearson’s chi-squared test, as appropriate. A P value <
0.05 was used as the cutoff for statistical significance.
EDGE algorithm (11). Multiple hypothesis testing was addressed by
false discovery analysis using a Q-value of < 0.01 (11). When a protein
was represented by more than one spot on the gel, the abundance values
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