Metabolic Profiling Regarding Pathogenesis of Idiopathic Pulmonary

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Metabolic profiling regarding pathogenesis of Idiopathic Pulmonary Fibrosis Yun Pyo Kang, Sae Bom Lee, Ji min Lee, Hyung Min Kim, Ji Yeon Hong, Won Jun Lee, Chang Woo Choi, Hwa Kyun Shin, Dojin Kim, Eun–Seok Koh, Choon-Sik Park, Sung Won Kwon, and Sung-Woo Park J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00156 • Publication Date (Web): 07 Apr 2016 Downloaded from http://pubs.acs.org on April 9, 2016

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Journal of Proteome Research

Metabolic profiling regarding pathogenesis of Idiopathic Pulmonary Fibrosis Yun Pyo Kang1, Sae Bom Lee1, Ji min Lee2, Hyung Min Kim1, Ji Yeon Hong1, Won Jun Lee1, Chang woo Choi3, Hwa Kyun Shin3, Do-Jin Kim2, Eun–Seok Koh4, Choon sik Park2, Sung Won Kwon1,*, and Sung-woo Park2,* 1

College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea 2

Division of Allergy and Respiratory Medicine, Department of Internal Medicine,

Soonchunhyang University Bucheon Hospital, 1174, Jung- Dong, Wonmi-Ku, Bucheon, Gyeonggi-Do, 420-767, Korea. 3

Department of Thoracic and cardiovascular surgery, Soonchunhyang University Bucheon Hospital, 1174, Jung- Dong, Wonmi-Ku, Bucheon, Gyeonggi-Do, 420-767, Korea.

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Department of Pathology, Soonchunhyang University Bucheon Hospital, 1174, Jung- Dong, Wonmi-Ku, Bucheon, Gyeonggi-Do, 420-767, Korea.

* Corresponding Authors: Dr. Sung-Woo Park, Telephone: +82-32-621-5105, Fax: +82-32621-5023, E-mail: [email protected]; Sung Won Kwon, Ph. D., Telephone: +82-2-880-7844, Fax: +82-2-886-7844, E-mail: [email protected] 1

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ABSTRACT Idiopathic pulmonary fibrosis (IPF) is a progressive, eventually fatal disease characterized by fibrosis of the lung parenchyma and loss of lung function. IPF is believed to be caused by repetitive alveolar epithelial cell injury and dysregulated repair process including uncontrolled proliferation of lung (myo) fibroblasts and excessive deposition of extracellular matrix proteins in the interstitial space; however, the pathogenic pathways involved in IPF have not been fully elucidated. In this study, we attempted to characterize metabolic changes of lung tissues involved in the pathogenesis of IPF using gas chromatography-mass spectrometry based metabolic profiling. Partial least square discriminant analysis (PLS-DA) model generated from metabolite data was able to discriminate between the control subjects and IPF patients (R2X = 0.37, R2Y = 0.613 and Q2 (cumulative) = 0.54, receiver operator characteristic AUC > 0.9). We discovered 25 metabolite signatures of IPF using both univariate and multivariate statistical analyses (FDR < 0.05 and VIP score of PLS-DA > 1). These metabolite signatures indicated alteration in metabolic pathways; adenosine triphosphate degradation pathway, glycolysis pathway, glutathione biosynthesis pathway, and ornithine aminotransferase pathway. The results could provide additional insight in understanding the disease and potential for developing biomarkers.

KEYWORDS Idiopathic pulmonary fibrosis; lung tissue; gas chromatography-mass spectrometry (GC-MS); metabolic profiling

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INTRODUCTION Idiopathic pulmonary fibrosis (IPF) is a progressive, lethal lung disease characterized by alveolar epithelial cell injury, proliferation of activated fibroblasts and myofibroblasts, and extracellular matrix accumulation/remodeling that leads to irreversible distortion of the lung parenchyma 1, 2. Several lines of previous studies suggested that the disease is related with the abnormalities in multiple biological processes such as clotting cascade, oxidant–antioxidant pathway, apoptosis, angiogenesis, and vascular remodeling 3-5. Although genetic perturbations and associated molecular mechanism involved in the development of pulmonary fibrosis have been reported 6-8, underlying pathophysiology of IPF still remains poorly understood. Moreover, a number of biomarkers were discovered, however their diagnostic and predictive utility is unclear 9. Metabonomics and metabolomics are based on the global profiling of metabolites in a biological system 10, 11. Metabolic profiling of biofluids and tissues provides information of abundance changes in endogenous metabolites to complement transcriptomics and proteomics in observing cellular response to disease 12. Identification of changes in metabolites in biological samples from the patients may facilitate understanding of precise underlying metabolic pathways of the disease. Metabolic profiling has been applied to the respiratory diseases such as asthma, COPD, and cystic fibrosis 13-16; however, the metabolic outcomes of lung tissue of IPF are remained to be characterized. We hypothesize that investigation of metabolic profiles of patients with IPF, especially localized in the lung, will support clear understanding of metabolic milieus which may influence the initiation or progression of fibrosis. In this study, we analyzed metabolites in lung tissues of 3



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IPF using gas chromatography-mass spectrometry (GC-MS) and characterized the metabolite signatures that might be involved in the pathogenesis of lung fibrosis.

EXPERIMENTAL SECTION Detailed internal spectral library of authentic standards of metabolites, statistical analysis results, and metabolic profiling data processing procedure are shown in supporting information.

Ethics statement The study was approved by the Internal Board Committee of Soonchunhyang University Bucheon Hospital (SCH-IRB-09-30), and the methods were carried out in accordance with the approved guidelines. The written informed consent was signed and obtained from all subjects.

Human subjects and lung tissue samples All human lung tissue samples of patients with IPF (n = 13) and the control subjects (n = 15) were obtained from the Biobank of Soonchunhyang University Hospital, sponsored by the Health and Welfare Ministry. The diagnosis of IPF was based on the international consensus statement by the American Thoracic Society and the European Respiratory Society 17. Surgical lung biopsy specimens were obtained from two different segments, which were identified via high-resolution computed tomography (HRCT). The histologic diagnosis of usual interstitial pneumonia (UIP) was confirmed by two pathologists. The control samples, non-diseased lung specimens were obtained from the patients who underwent surgery for early lung cancer. The 4


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clinical data included patients’ clinical information, pulmonary function tests, blood gas analyses, and smoking exposure histories (Table 1).

Sample preparation The sample preparation method was based on previous papers 18-20. Briefly, twenty-eight lung tissue specimens from patients with IPF and control subjects were carefully weighed to 10 mg. Seven internal standards (alanine d7, glycine d5, citric acid d4, stearic acid d35, tryptophan d5, fructose 13C6, and benzoic acid d5) 18 were added to a solvent mixture, consisting of chloroformmethanol-water (2:5:2, v/v/v) 19, to a final concentration of 3.5 µg/mL. Subsequently, tissue metabolites were extracted with 1 mL of mixture of internal standards and solvent via ultrasonication in a water bath for 100 min at ambient temperature (24 ~ 28ºC) 19. The samples were subsequently centrifuged at 16,000 g and 20°C for 5 min. The upper layer (800 µL) of samples was transferred to eppendorf tubes, and the remaining extracts were collected to prepare the pool of quality control (QC) sample. The 800 µL of experimental and QC samples were evaporated under a nitrogen stream at 50°C. 100 µL of toluene was added into the dried samples and subsequently evaporated under a nitrogen stream at 50°C to complete dryness 19. The metabolite derivatization was performed as following 20: the dried samples were methoximated by resuspending in 60 µL of 15 mg/mL methoxyamine-HCl in pyridine and incubating for 90 min at 30°C. Afterwards, the samples were trimethylsilylated by adding 60 µL of trimethylsilylating (TMS) reagent [N, O-bis(trimethylsilyl)trifluoroacetamide (BSTFA)/trimethylchlorosilane (TMCS), (99:1, v/v)] and incubating for 15 min at 70°C. After cooling at ambient temperature for 30 min and centrifuging at 16,000 g for 5 min, 80 µL of all 5



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supernatants were subjected to GC-MS analysis.

GC-MS data acquisition The experimental samples were analyzed in a random order with QC samples. The GCMS condition was based on the method previously published by Nishiumi, S. et al. 21. One microliter of the derivatized samples were injected into a gas chromatography-mass spectrometer (GC-2010 with GC-MS QP2010, Shimadzu, Germany). The injection temperature and the split ratio were 270°C and 1:2, respectively. A DB5 column (30 m length, 0.25 mm internal diameter, 0.25 µm film thickness, J&W Agilent technologies) was used to separate the metabolites, and helium was used as carrier gas. The temperature gradient condition was set as following: the initial temperature was set at 80°C for 2 min, then increased to 300°C at a rate of 5°C/min, and maintained for 5 min. The mass spectrometry parameters were set as following: electron impact was used as an ionization source, and the electron energy level was 70 eV. The ion source temperature and interface temperature were set to 200°C and 300°C, respectively. Data were acquired in scan mode from 30~600 m/z, with a scan time of 0.2 sec.

Metabolite identification Using the NIST MS database, a list of metabolite candidates was generated, and the identity of each peak was confirmed by electron ionization mass spectrometry (EI-MS) spectra and retention time of authentic standards (Table S1).

GC-MS data processing 6


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The GC-MS data were converted to netCDF format, and the baseline correction, data smoothing, noise reduction, peak extraction and peak alignment were processed using MetAlign 22

. The processed data were exported as .CSV files consisting of peaks with their retention time,

mass value, and intensity. The intensities of multiple peaks belonging to the same metabolite or the isotope-labeled internal standard were integrated, and the integrated peak area ratios of metabolite to each of internal standards were calculated respectively. Alanine, glycine, citric acid, stearic acid, and tryptophan were normalized by their isotope-labeled internal standard. For other metabolites, the internal standard offering the lowest relative standard deviation (RSD, %) in all QC samples was selected as the internal standard to normalize that metabolite in all the study samples 18. The metabolite peaks with high variations (RSD > 30% in QC samples) were excluded in statistical analysis (Table S2) 18, 23.

Statistical analysis The peak table of metabolites with RSD < 30% was applied to statistical analyses. The Wilcoxon rank-sum test and Benjamini-Hochberg false discovery rate (FDR) analysis were performed by MetaboAnalyst (http://www.metaboanalyst.ca) 24. The metabolites with FDR < 0.05 were considered as statistically significant. Additionally, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed using SIMCAP+ 12 (Umetrics, Umeå, Sweden) 25. The data were mean-centered and unit variance scaled prior to the PCA and PLS-DA 26. The significant PLS components were calculated by a 7-fold crossvalidation 27. The validity of the PLS-DA model was assessed using a permutation test with 100 iterations and an analysis of variance of the cross-validated predictive residuals (CV-ANOVA) 28, 7



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. The predictive performance of the PLS-DA model was evaluated by the receiver operator

characteristic curve (ROC) analysis in GraphPad Prism 5.04 (Graph Pad Software Inc., San Diego, USA) using the cross-validated predicted Y-values 19, 28. The variable importance in projection (VIP) scores of each metabolite were calculated in the PLS-DA model, and the metabolites with VIP scores greater than one were considered as significant. The metabolite signatures that were significant in both univariate and multivariate statistical analyses (FDR < 0.05 and VIP > 1) were subjected to ROC curve analysis using MetaboAnalyst 24. After normalization of the metabolite signatures data set via logarithmic transformation and autoscaling (mean-centered and divided by standard deviation of each variable), ROC curve analysis was performed using a linear support vector machine (SVM)-based algorithm. The area under the ROC curve was evaluated using Monte Carlo cross-validation (MCCV) with 100 iterations, and the significance of model was validated by 1000-iteration permutation test 30. The clinical data were expressed as medians (25th-75th quartile). Either the Wilcoxon rank-sum test or the chi-squared test was used to compare the differences between the IPF and the control groups. P value < 0.05 was considered statistically significant.

The determination of γ-GCS, GSS, OAT and ATP levels in the lung tissue Levels of human γ-glutamylcysteine synthetase (γ-GCS) (sample number; control = 12, IPF = 12), glutathione synthetase (GSS) (sample number; control = 12, IPF = 11), and ornithine aminotransferase (OAT) (sample number; control = 12, IPF = 12) in the lung tissues were determined by enzyme-linked immunosorbent assay (ELISA) (Figure S1). A γ-GCS kit (BD Biosciences, Bedford, MA, USA), GSS kit (Cusabio Biotech, Wuhan, China) and OAT kit (R&D 8


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Systems, Minneapolis, MN, USA) were used according to manufacturers’ recommendations. Intracellular ATP levels (sample number; control = 15, IPF = 13) were determined by a colorimetric method using an ATP assay kit (Abcam, Cambridge, MA, USA) following manufacturer’ recommendation. All of their cellular levels were normalized to protein quantity.

Gene expression level and correlation analysis of OAT The processed microarray data and clinical information from previous report were obtained from ArrayExpress (Accession number: E-GEOD-32537) 31. The OAT gene expression levels were compared between the control subjects (n = 50) and the patients with IPF (n = 119) using Wilcoxon rank-sum test. The correlation analysis between OAT gene expression levels and pulmonary function (% predicted forced vital capacity, FVC) of patients with IPF (n = 117) was performed using a Spearman’s correlation analysis in GraphPad Prism 5.04.

RESULTS AND DISCUSSION The demographic and physiological characteristics of the study subjects The lung tissues used in this study were obtained from 13 patients with IPF and 15 control subjects. There were no significant differences in age, gender and smoking history between the two groups (Table 1). However, the IPF group had significantly lower levels of % predicted forced vital capacity (FVC), diffusing capacity of carbon monoxide (DLCO) and arterial partial oxygen pressure (PaO2) levels than the control group (Table 1).

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GC-MS data analysis The GC-MS based metabolic profiling study on human lung tissue specimens was performed as described in Figure S1. A total of 61 metabolites were identified using authentic standards (Table S1), and 54 metabolites were suitable for the statistical analysis (Table S2). The PCA scores plot exhibited no considerable outliers which may influence the results 24, and a distinctive separation pattern between the patients with IPF and the control subjects was observed (Figure 1A). As a result of 7-fold cross validation, only one PLS component was significant (Q2 > 0.05) 27; other components (component 2 and 3) had Q2 values of -0.128 and 0.439, respectively. The performance statistics of the cross-validated PLS-DA model were R2X = 0.37, R2Y = 0.613 and Q2 (cumulative) = 0.54, indicating a good separation between the two groups (Figure 1B). Sensitivity and specificity of the cross-validated PLS-DA scores plot were 91.7% and 93.3%, respectively. The result of CV-ANOVA indicated that the model was statistically significant (P value < 0.0001). Plots of a 100-iteration permutation test showed that most of the permuted R2values on the left were lower than the original point on the right, and the Q2 regression line had a negative intercept (Figure 1C) 28. The area under the ROC curve generated from the cross-validated predicted-Y values was above 0.9, showing good predictive performance of the PLS-DA model (Figure 1D) 19, 28. Since only one significant PLS component was determined by cross-validation, the model over-fitting was not an issue when selecting optimal number of PLS-components in this model. However, in future studies, a double crossvalidation should be preferred in order to avoid possible bias 32. Using the variable importance in projection (VIP) scores of individual metabolites obtained from the established PLS-DA model, we identified 26 out of 54 metabolites as significant (VIP > 1) (Table S2). The results of 10


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Wilcoxon rank-sum test with FDR analysis also showed that 28 out of 54 metabolites (FDR < 0.05) were significantly different between patients with IPF and control subjects (Table S2). Altogether, a total of 25 metabolite signatures were identified via both the univariate and the multivariate statistical analyses (FDR < 0.05 and VIP > 1) (Table S2). The ROC curve generated from these 25 metabolite signatures showed the area under the curve above 0.9, which was validated by permutation test (P value < 0.05), indicating an excellent discriminative performance between patients with IPF and control subjects (Figure S2A and B).

Metabolite signatures reflecting aberrant metabolism in IPF In IPF lung tissue, we observed increased levels of inosine and hypoxanthine, which are breakdown products of ATP 33, 34, and these may reflect the depletion of intracellular ATP (Figure 2A). To validate this finding, we measured the intracellular level of ATP in lung tissues, and it was decreased in IPF (Figure 2B). Numerous biochemical events such as oxidative stress and hypoxia can lead to mitochondrial damage resulting in depletion of ATP and also inappropriate release of cytochrome C, thus inducing pathways of apoptosis 35. Bueno and coworkers recently described that the significant accumulation of dysfunctional mitochondria in alveolar type II cells of IPF lung was related to development of lung fibrosis 36. Taken together, the result suggests that ATP is depleted in IPF lung (Figure 2C), and this may contribute to the vulnerability to oxidative stress and cellular dysfunction. Our metabolic profiling results showed that glucose was decreased, and glycolytic intermediate metabolites (glucose-6-phosphate, dihydroxyacetone phosphate, and lactic acid) and product to substrate ratios of glucose-6-phosphate to glucose and lactic acid to pyruvic acid were 11



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increased in IPF lung tissues (Figure 3A and B), suggesting glycolysis is activated (Figure 3C). Under hypoxic conditions, cells reduce their rates of aerobic metabolism and increase glucose consumption dramatically in order for anaerobic glycolysis to generate ATP; lactic acid is produced as a byproduct 37. Even in the absence of hypoxia, normal proliferating cells and cancer cells display high rates of aerobic glycolysis in which the majority of the glucose is converted to lactic acid 38, 39. The accumulated lactic acid induces the differentiation of myofibroblasts via activation of latent transforming growth factor (TGF)-β in IPF 40. Recently, Win et al. reported that normal lung parenchyma on HRCT exhibited increased 18F-FDG PET signals in the setting of IPF 41, indicating accelerated glycolysis in IPF lung which is in concordance with our metabolic profiling results. Both human and in vivo studies have exhibited that marked elevation of oxidant burden and disturbed oxidant/antioxidant balance are implicated in the pathogenesis of IPF 5. Glutathione (GSH) is the most abundant low molecular-weight thiol, and GSH/glutathione disulfide is the major intra- and extracellular protective antioxidant in the lungs 42. Previous study reported that the level of GSH was lower in the epithelial lining fluid in the lungs of IPF 43. The synthesis of GSH from glutamate, cysteine, and glycine, involves sequential action of two cytosolic enzymes, γ-glutamylcysteine synthetase (GCS) and glutathione synthetase (GSS) 44. In this study, the metabolite snapshot showed that cysteine, glycine and glutamic acid were increased in IPF (Figure 4A). Since these three amino acids are components of tripeptide form of GSH, we hypothesize that increased levels of these three amino acids might be due to decreased activities of GSH synthesizing enzymes in IPF. We measured the levels of γ-GCS and GSS in the lung lysates, and they were both reduced in IPF (Figure 4B). Collectively, the results indicate 12


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that GSH biosynthesis is impaired in IPF lung tissues (Figure 4C), and the reduced levels of GSH producing enzymes may explain decreased GSH levels in the IPF lung. Collagen fibrils are the most abundant protein in extracellular matrix (ECM), and the excess of collagen deposition in ECM is associated with the progression of IPF 45, 46. Collagen synthesis is a proline-dependent process, and arginase has been regarded as the key enzyme for the proline dependent collagen biosynthesis in fibrosis 47. Arginase uses arginine as a substrate to produce ornithine, and through ornithine aminotransferase (OAT), ornithine in turn converts into proline which is a major component of collagen 47. A previous study reported that arginase-1 mRNA and protein expression were increased in bleWomycin-induced pulmonary fibrosis mice, but arginase-1 expression was significantly decreased in the lung tissue of IPF patients compared to control subjects 48. Importantly, our metabolic profiling results showed that the abundance of proline and the ratio of proline to ornithine were increased in patients with IPF (Figure 5A and B). The OAT level, measured using a quantitative enzyme assay, was increased in IPF lung tissues (Figure 5C). We further analyzed expression of OAT gene in publicly available lung tissue microarray datasets (50 control subjects and 119 IPF patients) 31. This gene expression level was significantly higher in patients with IPF (P < 0.0001) (Figure 5D). Additionally, there was a negative correlation between OAT gene expression level and the % of predicted FVC (Spearman’s correlation coefficient = -0.3456, P = 0.0001, Figure 5E); the lower FVC values are related with poor prognosis of IPF 49. Collectively, these data indicate that proline production via OAT pathway is activated in IPF (Figure 5F) and signify the potential role of OAT in IPF pathophysiology, which has not been highlighted previously.

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CONCLUSIONS This study emphasizes the advantage of metabolic profiling for characterizing metabolite signatures of IPF lung and changes in metabolic pathways which might be involved in pathogenesis of the disease. These results may helpful for understanding of metabolic mechanism of human lung fibrosis. However, a limitation of the current study is the small cohorts. For developing diagnostic and prognostic biomarkers of IPF, further functional studies and validation in large sample are required.

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Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning [Grant 2011-0029572, 2014R1A2A2A01007383] and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 20090083533). We are grateful to Ms. Myoung Ran Lee and Eun young Kim for their aid in the collection of lung tissues.

Author Contributions: S.W.K and S.W.P contributed equally to this work. Conflict of Interest Disclosure: The authors declare that they have no conflicts of interest with the contents of this article.

ASSOCIATED CONTENT Supporting Information Table S1 - EI-MS spectrum of 61 authentic standards of metabolites Table S2 - Statistical analyses of metabolites in control and IPF Figure S1 - Workflow of study Figure S2 - ROC curve of metabolite signatures

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FIGURE LEGENDS Figure 1. (A) Principal component analysis (PCA) scores plot and (B) cross-validated partial least squares discriminant analysis (PLS-DA) scores plot for comparison of control subjects (n=15) and patients with IPF (n=13). (C) Validation plot from 100 permutation test and (D) receiver operating characteristic (ROC) curve generated from cross-validated predicted Y values of PLS-DA model. AUC, area under the curve

Figure 2. Enhanced ATP degradation in IPF lung. The comparison of (A) ATP metabolites and (B) ATP levels between control subjects and patients with IPF. The values represent the mean with 95% CI (A and B). (C) ATP is depleted in IPF lung tissue.

Figure 3. Active glycolysis in IPF lung. (A) The comparison of glycolysis metabolites, and (B) the ratios of glucose-6-phosphate to glucose and lactic acid to pyruvic acid between control subjects and patients with IPF. The values represent the mean with 95% CI (A and B). (C) Glycolysis is activated in IPF lung tissue.

Figure 4. Impairment of GSH biosynthesis in IPF lung. The comparison of (A) GSH biosynthetic metabolites and (B) GSH synthesizing enzymes between control subjects and patients with IPF. The values represent the mean with 95% CI (A and B). (C) GSH biosynthesis is impaired in the lung tissues of IPF. γ-GCS, γ-glutamylcysteine synthetase; GSS, glutathione synthetase

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Figure 5. Increase of proline production via OAT pathway in IPF lung. The comparison of (A) proline, (B) the ratio of proline to ornithine, (C) OAT protein, and (D) OAT gene expression between control subjects and patients with IPF. The values represent the mean with 95% CI (A~D). (E) Spearman’s correlation analysis between OAT gene expression and FVC (%) of IPF. (F) Proline production via OAT pathway is increased in IPF lung tissue. OAT, ornithine aminotransferase; FVC, forced vital capacity

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FIGURES Figure 1.

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Figure 2.

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Figure 3.

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Figure 4.

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Figure 5.

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Table 1. Demographic characteristics of study subjects IPF (n = 13)

Control (n = 15)

Age (years)

61.5 (52-74)

56.0 (32-75)

Male/Female (N)

9/4

9/6

SM (N)

5

5

ES (N)

3

4

NS (N)

5

6

FVC (%)*

71.5† (55-96)

86.0 (77-101)

FEV1 (%)*

81 (67-121)

105 (84-112)

FEV1/FVC (%)*

83.0 (77-94)

83.5 (77-87)

DLCO (%)*

68† (43-75)

91 (77-113)

PaO2 (mmHg)

65† (56-92)

86(72-109)

Total cells, 105/mL

120.0† (16-171.5)

19.5 (4.2-28)

Macrophage (%)

79.0 (68.2-82.8)

88.7 (63.8-95.2)

Neutrophils (%)

10.2† (1.8-25.4)

2.7 (0.4-6.05)

Eosinophils (%)

1.2 (0.8-6)

0 (0-1.2)

Lymphocytes (%)

3.8 (1.2-6.6)

4.1 (1-6.2)

Epithelial cells (%)

5.8 (0-7.8)

4.5 (0-12)

Smoking

BALF

Data are presented as N, percentage or *: median (25th-75th percentile). SM: current smoker, ES: ex-smoker, NS: never smoker. †: P < 0.05 compared with control subjects

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For TOC only

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Metabolic Profiling Regarding Pathogenesis of Idiopathic Pulmonary

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