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Rapid proteome analysis of bronchoalveolar lavage samples of lifelong smokers and never-smokers by micro-scale liquid chromatography and mass spectrometry.

The advent of highly sensitive protein separation instruments coupled with mass spectrometric identification has allowed detailed analysis of the total protein expression profiles in a variety of clinical samples, including biofluids and tissue. There is increasing evidence that specific patterns of protein expression occurring in biofluids (e.g., plasma, serum, and urine) and tissue (e.g., biopsies and surgical resections) may serve as diagnostic or prognostic measurements of disease status and thus relate to clinical phenotype (1-4). As such, combined efforts by international initiatives, such as the Human Proteome Organization, in academic and commercial settings are underway to catalog the protein components of the many compartments of samples available for study.

To explore the context of global protein expression patterns in complex clinical samples, one frequently used strategy involves an approach known as "shotgun sequencing", which allows the rapid identification of hundreds of protein identities present in high to medium abundance (5-7). The relative sensitivity for accurate identification of the thousands of peptides in these separations has recently improved with the development of new paradigms in mass spectrometry (MS) (5) design. Thus, commercial instruments such as the linear ion trap quadropole (LTQ) mass spectrometer and the LTQ orbitrap hybrid mass spectrometer have contributed to greater sensitivity and accuracy in the detection of peptide masses in separated peaks.

Bronchoalveolar lavage (BAL) is a clinical biofluid sampling of the soluble protein contents of the airway lumen. The lavage procedure is often used to evaluate conditions of upper airway inflammation, allergic airway disease, or respiratory tract malignancies. We and others have reported on the relative abundances of proteins present in BAL fluid, based on 2-dimensional gel separation and on the identification of excised spots by MS, in clinical diseases such as asthma, sarcoidosis, idiopathic fibrosis, and interstitial lung disease (8-12). In a recent study that compared the BAL protein profiles obtained by 2-dimensional gel analysis in lifelong smokers and never-smokers, we identified common qualitative and quantitative differences among individuals grouped by smoking histories (13, 14). These included proteins with potentially important biological activities related to inflammation, oxidation-reduction, tissue matrix turnover, and immunity.

Although the 2-dimensional gel electrophoresis system described above can consistently identify proteins present in medium to high abundance in BAL samples, we were interested in determining whether the shotgun sequencing technology might achieve additional protein identification sensitivity. In the present study, we used the shotgun approach coupled with 1-dimensional reversed-phase nano-HPLC coupled on-line with LTQ MS (15-18) to analyze the protein composition in BAL fluid. Using pooled aliquots of BAL samples from lifelong or never-smokers, we investigated whether there are obvious differences in protein profiles in each group. To validate these findings, we analyzed individual BAL samples from each of the study participants in the same system to identify and quantify relative differences in expression profiles associated with smoking history. Finally, individual differences in protein expression seen by LTQ analysis were confirmed by 2-dimensional gel analysis of similar aliquots from the same participants.

Material and Methods


Men 60 years of age were recruited from the randomized population study "Men born in Gbteborg in 1933" (19, 20). A subset of the study population of 879 participants volunteered to undergo fiberoptic bronchoscopy at the age of 60. This group included 48 men: 30 asymptomatic chronic smokers (15 light and 15 heavy smokers) and 18 healthy never-smokers. The characteristics of the study participants are shown in Table 1. At the time of sampling, all of the study participants were clinically evaluated as being healthy because they had not sought medical attention for respiratory disease. All study participants included were evaluated by several respiratory function tests, including forced expiratory volume in 1 s, diffusion capacity of the lung for carbon monoxide transfer, total lung capacity, and vital capacity. Both the never-smokers and smokers showed ventilatory function results that fell within the reference intervals. Overall, however, smokers showed lower lung function values than the never-smokers.


BAL sampling was carried out under standard conditions (20) with the informed consent of the individuals, and the studies were approved by the ethics committees of the Sahlgrenska Hospital (Gbg M 117-01) and Lund University Hospital in Sweden (LU 689-01). After sampling, the BAL fluid was immediately transported on ice to the laboratory for processing and storage at -80[degrees]C (20). The protein concentrations in the recovered BAL fluids were determined by the Coomassie Plus protein assay with bovine serum albumin as the calibrator (21). The total protein concentrations in the recovered BAL samples were not significantly different among individuals, but the recovery of BAL fluid was lower in the smoking cohort (Table 1).


Pooled samples were prepared by combining equal amounts of protein from individual samples and were adjusted to a final quantity of 50 [micro]g. The proteins were digested with trypsin, according to a previously described procedure (22).


The peptides were separated on a [C.sub.18] nano-capillary column [Magic C18; 150 x 0.075 mm (i.d.); packed in house] on an Ettan multidimensional liquid chromatography (LC) system. The flow rate was maintained at 400 nL/min. The gradient was started at 2% acetonitrile containing 1 mL/L formic acid in water, increased to 35% acetonitrile in 60 min, then increased to 60% acetonitrile in 15 min, and finally, increased to 90% acetonitrile in 10 min. Of each sample, 2 [micro]g was injected on the column by the auto sampler. The resolved peptides were detected on an LTQ mass spectrometer (Thermoelectron) with a nanoelectrospray ionization ion source. To provide consistency, as proposed by Washburn et al. (23), each pooled sample was analyzed in triplicate.


We applied as closely as possible the proposed publication guidelines for the analysis and documentation of peptide and protein identifications (24, 25). The peptide sequences generated by LTQ MS were identified by correlation with the peptide sequences present in the nonredundant National Center for Biotechnology Information protein database (TaxonomyID=9606, available at (26), which contains Swiss-Prot protein entries, using the Sequest algorithm that is incorporated in the BioWorks[TM] software (Ver. 3.1 SR; Thermoelectron).

To estimate relative protein abundances, we considered the number of peptides leading to identification and the semiquantitative Sequest score parameter in conjunction with peak-area measurements. Peak-area measurements were performed on abundant peptides. We extracted the peak area of the m/z signal of a selected peptide for a given protein within 0.5 min of a given retention time, from where the peptide was identified in the LC-MS chromatogram. The peak in the extracted ion chromatogram was identified when the signal-to-noise ratio was >3. The cross-correlation (Xcorr) values for each peptide were inspected, and if an individual value showed significant deviation, the spectrum obtained by tandem MS (MS/MS) was inspected manually.



To test the reproducibility of the separation and detection system, we performed nano-HPLC separations, in triplicate, on aliquots (2 [micro]g) of the trypsin digests of the unfractionated pooled BAL samples in the LC-LTQ system. The replicates run in triplicate showed very similar peak pattern distributions vs retention time (see Fig. 1 in the Data Supplement that accompanies the online version of this article at We selected 10 peaks, from 3 consecutive runs, representing polar, medium-polar, and nonpolar (hydrophobic) protein sequences in the portion of the chromatogram in which most of the peptides eluted (31-66 min). The typical relative standard deviation (RSD) for polar protein sequences ranged from 0.2% to 1.7%, the RSD for medium-polar sequences ranged from 0.8% to 2.5%, and the RSD for nonpolar protein sequences ranged from 1.0% to 2.0%. We thus concluded that the shotgun approach applied to this separation platform was consistent with our aim of establishing a robust and reproducible rapid protein identification system.


A direct comparison of the chromatographic profiles of pooled BAL fluids from never-smokers, light smokers, and heavy smokers, as separated by capillary LC, is shown in Fig. 1. The pattern for light smokers more closely resembled the pattern for never-smokers than the pattern for heavy smokers. These differences were particularly apparent in the peak distributions in the 40- to 60-min window (nonpolar separation).

When we used the requirement of [greater than or equal to] 2 peptides per identification, the 90-min LC/LTQ platform (0.4 s/scan) identified 268 proteins in the pooled BAL samples from never-smokers, 309 proteins in the pooled samples from light smokers, and 314 proteins in the pooled samples from heavy smokers. Approximately one third (n = 130) of all proteins identified were identified in all 3 groups. However, we also observed that a substantial number of proteins were identified in either the samples from smokers (n = 137) or never-smokers (n = 63). These groups of unique proteins included both high-abundance and medium-abundance proteins. The majority of these proteins have not been reported previously in BAL fluid. The 5 most abundant proteins corresponded to generally recognized major components of BAL fluid: albumin, transferrin, [[alpha].sub.1]-antitrypsin, IgA, and IgG. In the case of albumin, the protein was identified with 83% sequence coverage, whereas for transferrin and [alpha].sub.1]-antitrypsin, the proteins were identified with 69% and 56% sequence coverage, respectively. These proteins were present in samples from all 3 groups. IgA and IgG were identified by their heavy and light chains, where each individual chain had a sequence coverage between 30% and 90%. Typical RSD values for the high-abundance proteins albumin, transferrin, and IgG, based on triplicate runs, were 6.7%, 15%, and 26% for the never-smokers and 3.1%,17%, and 22% for the heavy smokers.



To evaluate relative changes in protein expression corresponding to chronic exposure to cigarette smoke, we compared protein concentrations in never-smokers and in chronic smokers. The comparison included the Sequest score as well as differences in total peptide ion intensity of a selected peptide fragment.

As an example of quantitative regulation, the Sequest score and the number of peptide sequencing events showed an up-regulation of UMP-CMP kinase among the heavy smokers. This up-regulation was confirmed by comparison of peak areas of the peptide KNPDSQYGELIEK (m/z 760.8) for UMP-CMP kinase, as presented in Fig. 2 of the online Data Supplement. It represents a 13-fold up-regulation of UMP-CMP kinase among the heavy smokers. Peak-area measurements from triplicate analyses gave an RSD of 12%. The peak areas of selected peptides used for preliminary quantification of the proteins are shown in Table 2. As an example of significant regulations in terms of presence-absence, the Sequest score and the number of peptide sequencing events showed up-regulation of cathepsin D among the smokers and of glutathione S-transferase A2 among the heavy smokers. Cathepsin D was below the detection limit in samples from the never-smokers, and glutathione S-transferase A2 was below the detection limit in samples from the light and never-smokers. These cases of presence--absence were confirmed by comparison of peak areas of the peptide LLDIACWIHHK (m/z 703.8) for cathepsin D and the peptide NDGYLMFQQVPMVEIDGMK (m/z 855.3) for glutathione S-transferase A2.

An example of the identification of a doubly charged peptide obtained by MS/MS is shown in Fig. 2. In this example, the peptide sequence KAYINTISSLKDLITK of the precursor ion (m/z 905.3) of A-kinase anchor protein 9 was identified.


We were interested in determining how well the consensus protein profiles obtained with pooled samples compared with the possible ranges of expression present in individual samples. To determine the relative abundances and presence rates of the BAL proteins identified in the pooled samples, we ran 2 [micro]g of BAL sample from each of the 48 study individuals separately on the LC-MS/MS platform. A comparison of the number of protein identities found in pooled or individual samples in neversmokers and heavy smokers is shown in Fig. 3. We observed a wide variation in the total numbers of proteins that could be identified in a given sample (range, 48-314), irrespective of smoking history. The pooled samples achieved higher numbers of protein annotations than any of the individual samples. This is likely the result of an additive effect from several of the group members because of the lack of obvious outliers and the tight clustering seen in the distributions of the number of identities detected in both groups.


In general, the patterns of common or unique protein identities observed in each of the group pools were also maintained by the individuals within the group. Analysis of individual samples allowed us to notate the relative presence rates of the individual identifications in the different samples. As an example, Table 3 shows representative examples of BAL proteins commonly identified in samples from both smokers and never-smokers as well as proteins found only in samples from smokers. We compared the number of peptides identified in the pooled samples with the range of peptides found in individuals and found close agreement. We found no evidence that the pooled protein profiles were skewed because of the contribution of singular individuals who contributed a dominant selection of proteins to the pool. Altogether, these data indicate that the profiles for the pooled samples generally mirrored the entire group of individuals.



In an ongoing activity, performed in parallel to the LC-MS/MS, we analyzed the patterns of protein expression in BAL fluids in individuals, using 2-dimensional gel separation coupled with computer-assisted image analysis for spot localization and annotation and matrix-assisted laser desorption/ionization (MALDI)-MS for isolated spot protein identification (13). Using separate aliquots of the same sample, we were thus able to directly compare the relative presence and/or absence and the pixel intensity values of individual protein spots that corresponded to the protein identities obtained with the LC-MS/MS platform. We selected a set of high- to medium-abundance proteins on the gels and compared the relative spots in terms of their distributions on the individual gels, total pixel density of the spot areas, and the identity scores obtained with the Mascot sequence annotation software. Fig. 4 presents representative examples of the 2-dimensional electrophoresis spot patterns for proteins that had previously been identified in the pooled BAL samples by the LC-MS/MS platform. Shown are representative gel profiles of never-smokers or smokers. The Rho-GDP dissociation inhibitor protein, which was identified only in the smokers and not in the neversmokers by LC, showed a strong spot in the smokers and only a weak spot in the never-smokers (near the limit of detection). We found a similar concordant result for cathepsin D, which showed consistency in the relative detectable concentrations of protein on the 2 platforms. The last example shown, al-antitrypsin, showed a high relative abundance on the gels of both smokers and never-smokers. Together, the results of the direct comparison of the protein expression profiles of individuals obtained by both LC-MS/MS and 2-dimensional gel electrophoresis showed remarkable similarity to the relative distributions of proteins detected in the pooled samples.


The entire dataset of proteins identified in the pooled BAL samples was annotated with Gene Ontology (27, 28). Because proteins are commonly assigned to more than a single molecular function and biological process, we have taken this into consideration in Table 1 in the online Data Supplement. Accordingly, Fig. 3 in the online Data Supplement shows bar diagrams of annotated proteins identified by at least 2 peptides in the BAL fluid of the pooled samples. For graphical reporting, the proteins were categorized into molecular function and biological process.


In this report we present the first differential proteomic analysis of BAL fluid from lifelong smokers and neversmokers, using an approach known as shotgun sequencing. Our results show that the protein composition of BAL samples from smokers as a group is significantly different from that in BAL samples from age-matched never-smoking controls as a group. These differences were observed in the relative presence-absence scores, the relative areas of chromatographic peaks of specific proteins, and the numbers of peptides identified with specific proteins. There was also a trend for the lifelong smokers to have higher numbers of differing proteins in their BAL.

BAL fluid contains a wide variety of proteins that are either locally released by epithelial and inflammatory cells or through plasma exudation. Because of the diverse origin of BAL proteins, analysis of BAL fluid may reveal important pathologic mediators and may enable more accurate characterization of many lung diseases at the molecular level. As shown by the Gene Ontology chart (Fig. 3 in the online Data Supplement), there is a broad distribution of protein functional classes associated with different biological processes such as cellular physiologic processes, metabolism, response to stimulus, cell communication, localization, and organ development. This distribution classification is very different from what had been determined previously for plasma (29). One consideration to address from the BAL studies is that the group labeled "unknown" was found to be fairly large for both classifications. The reason for this is attributable to the large number of proteins observed in this study that are not annotated in Gene Ontology.


Pooling of the samples facilitated the initial identification of differentially regulated proteins in the neversmokers and smokers. Pooling also reduced the experimental variations in the data and minimized the data files subjected to computer-intensive comparative analysis. To validate the dataset obtained with pooled samples, we subjected samples from all of the study participants to individual separations/ analyses. Comparison of the results from the pooled and individual samples within each of the study groups showed remarkable similarities in the overall protein annotation indices of each respective group.

To evaluate relative changes in protein expression in response to chronic exposure to cigarette smoke, we used a semiquantitative approach. This included the Sequest score parameter and the number of peptide sequencing events, along with total peptide ion-intensity measurements of the selected peptide fragments. Quantification of proteins based directly on MS signal intensities without internal standards has historically drawn little attention. However, reports from several groups who used linear ion traps have shown that relative changes in total signal intensities of peptides correlated well with their concentration changes in one sample vs another (15-18). We found that individual differences in protein expression seen by LTQ analysis could be confirmed by 2-dimensional gel analysis of similar aliquots from the same individuals by protein spot intensity.

Altogether, our results showed that the LTQ platform is rapid (90-min run) and reproducible and that it can identify a high number of proteins and determine relative differences in global protein profiles from minimal starting sample volumes and protein concentrations; only 2 [micro]g of BAL proteins was needed for the analysis. Additionally, the separation did not require prefractionation to reduce the complexity of the BAL sample. However, limitations of the shotgun approach are that it detects only protein fragments and not intact proteins and therefore cannot discern isomeric forms of proteins and posttranslational modifications. When applying shotgun sequencing under semiquantitative conditions, one should use caution when comparing low-abundance proteins and proteins with small relative changes. For example, AKAP9 and RAB26 were observed among the neversmokers by a score of 40.1, with a 4-peptide hit, but they were not observed in the other groups. However, for the 3 groups, this situation showed no significant change in relative peak-area measurements. These results are consistent with other studies of low-abundance proteins that have shown that such proteins are not easily sequenced by MS/MS because of the time-dependent nature of the measurement (30). Another limitation is that the semiquantification is based on the number of peptides (with only some overlap between samples) and is therefore inclined to misrepresent absolute protein concentrations.

We believe that 2-dimensional gel and liquid-phase separations interfaced with MS are complementary approaches with different application in various biological settings, as well as being useful in combination with each other.

The reproducibility of the shotgun sequencing platform is governed by the peptide separation conditions (e.g., column length and diameter, packing, flow, and injection volume) and the subsequent MS detection. RSD values between peptide separations were typically 0.2%-2.5% for polar, medium-polar, and nonpolar protein sequences. Criteria used for protein MS identifications were the cross-correlation, which relates to database fit; scoring; and reproducible identification of the protein in 2 or more samples by the presence of 2 or more peptides in triplicate analyses. Biological variation among patients is also inherent in these assays and is a component of the reproducibility of the final results because of the large pulmonary surface area sampled by the BAL procedure and by the sample preparation. We minimized group variation by studying BAL samples from an age- and sex-matched cohort. The utility of studying BAL fluid is clinically important, and greater knowledge concerning its components holds potential value for measuring health and disease.

In conclusion, to our knowledge, we report here the most comprehensive database of the proteins present in BAL fluid from lifelong smokers and from never-smokers, using the approach known as shotgun sequencing.

We thank Krzysztof Pawlowski for discussions on Gene Ontology. The study was supported by The Swedish Heart Lung Foundation, The Swedish Society for Medical Research, and the Royal Physiographic Society in Lund.

Received September 17, 2005; accepted January 30, 2006.

Previously published online at DOI: 10.1373/clinchem.2005.060715


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* Address correspondence to this author at: AstraZeneca Research and Development, Scheelev. 8, Lund 22 187, Sweden. Fax 46-33-71-44; e-mail


[1] Barnett Institute, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA.

[2] Department of Respiratory Medicine and Allergology, University Hospital of Lund, Lund, Sweden.

[3] AstraZeneca, Department of Biological Sciences, Lund, Sweden.

[4] Department of Respiratory Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden.

[5] Nonstandard abbreviations: MS, mass spectrometry; LTQ, linear ion trap quadrupole; BAL, bronchoalveolar lavage; LC, liquid chromatography; MS/ MS, tandem mass spectrometry; and RSD, relative standard deviation.
Table 1. Characteristics of the study participants. (a)

 Smokers Never-smokers
 (n = 30) (n = 18) P

Lung function
 TLC,b% 97 (14) 96 (13) NS
 RV, % 116 (32) 94 (27) <0.05
 FEV1, % 92 (14) 107 (16) <0.001
 DLCO, % 84 (16) 95 (16) <0.05
Protein in BAL, 77 (40) 86 (38) NS
Recovery in BAL, 64 (30-110) 89 (50-120) <0.001
Smoking groups (c)
 Light (n = 15) 22 pack-years
 Heavy (n = 15) 45 pack-years

(a) Data are the mean (SD) or mean (range).
P value according to Mann-Whitney U-test.

(b) TLC, total lung capacity; NS, not significant;
RV, residual volume; FEV1, forced expiratory volume
in 1 s; DLCO, diffusion capacity of the lung for carbon
monoxide transfer.

(c) The definition of light smokers was 1-15 cigarettes
smoked per day with a median number of 22 pack-years
(range, 9-45 pack-years). The definition of heavy smokers
was [greater than or equal to] 15 cigarettes smoked per
day, with a median number of 45 pack-years
(range, 31-79 pack-years). Pack-years were calculated by
multiplying the number of packs of cigarettes smoked per
day by the number of years the person had smoked.

Table 2. Proteins showing regulatory differences
among the never-smokers and the smokers according to
the number of peptide hits, the Sequest protein
score, and peak areas.

ID (a) Protein name Peptide

KPYM Pyruvate kinase, isozymes M1/M2 LDIDSPPITAR
GDIR Rho GDP dissociation inhibitor 1 SIQEIQELDKDDESLRK
GSTA2 Glutathione S-transferase A2 NDGYLMFQQVPMVEIDGMK
AKAP9 A-Kinase anchor protein 9 KAYINTISSLKDLITK
SCN1A Sodium channel protein, brain FMASNPSK
 I [alpha]-subunit

 Hits (b) Scores (c)

ID (a) m/z NS (e) LS HS NS LS HS

KPYM 599.3 1 2 10 10.2 20.1 100.2
CATD 703.8 2 4 20.1 40.2
GDIR 682.9 1 5 10.1 50.2
KCY 760.8 1 6 10.1 60.2
GSTA2 855.3 5 50.2
AKAP9 905.3 4 40.1
SCN1A 883.0 4 40.1

RAB26 1081.8 4 40.1

 Peak areas (d)


KPYM 5 4.4 3.9 7.1
CATD 7 0.4 0.7
GDIR 6 0.2 0.5 1.3
KCY 7 0.5 0.6 6.5
GSTA2 8 0.4
AKAP9 9 0.2 0.2 0.4
SCN1A 810.1 0.3 0.3

RAB26 10 0.5 0.3 0.3

(a) The protein identification (ID) and name are
based on assignment from the nonredundant National Center
for Biotechnology Information protein database, which
contains Swiss-Prot protein entries, obtained with Sequest.
The nomenclature has been simplified to reflect the
identification of the protein.

(b) Number of redundant peptide identifications of the given

(c) Sequest protein score.

(d) Peptide peak area.

(e) NS, never-smokers; LS, light smokers; HS, heavy smokers.

Table 3. Examples of proteins identified in BAL
samples from study participants.

 Presence Mean (SD)
Protein Group rate (a) Sequest score

Annexin II Never-smokers 18/18 56 (32)
 Smokers 29/30 68 (26)
Serotransferrin Never-smokers 18/18 580 (184)
 Smokers 30/30 402 (196)
[[alpha].sub.1]- Never-smokers 18/18 249 (79)
Antitrypsin Smokers 30/30 164 (86)
Cathepsin D Never-smokers 1/18 10
 Smokers 15/30 36 (28)
Calmodulin Never-smokers 18/18 35 (18)
 Smokers 25/30 50 (20)
Insulin-like Never-smokers 3/18 10 (0)
growth factor- Smokers 19/30 18 (6)
binding protein

 Peptides Peptide
 identified, presence
Protein range in pool

Annexin II 1-7 10
 2-8 6
Serotransferrin 4-36 34
 3-38 44
[[alpha].sub.1]- 5-19 15
Antitrypsin 2-20 18
Cathepsin D 1 0
 1-8 2
Calmodulin 1-4 3
 1-4 3
Insulin-like 1 1
growth factor- 1-2 1
binding protein

(a) Presence rates among the never-smokers
(n = 18) and smokers (n = 30).
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Title Annotation:Proteomics and Protein Markers
Author:Plymoth, Amelie; Yang, Ziping; Lofdahl, Claes-Goran; Ekberg-Jansson, Ann; Dahlback, Magnus; Fehniger
Publication:Clinical Chemistry
Date:Apr 1, 2006
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Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization...
Proteomics: a new diagnostic frontier.

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