Separating Smoking-Related Diseases Using NMR-Based

Article pubs.acs.org/jpr

Separating Smoking-Related Diseases Using NMR-Based Metabolomics of Exhaled Breath Condensate Guglielmo de Laurentiis,†,‡,¶ Debora Paris,§,¶ Dominique Melck,¶,§ Paolo Montuschi,∥,¶ Mauro Maniscalco,† Andrea Bianco,⊥ Matteo Sofia,† and Andrea Motta*,§ †

Department of Respiratory Medicine, AO Monaldi, Faculty of Medicine, University of Naples “Federico II”, Naples, Italy Rehabilitation Center “Santa Maria del Pozzo”, Somma Vesuviana (Naples), Italy § Institute of Biomolecular Chemistry, National Research Council, Pozzuoli (Naples), Italy ∥ Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Rome, Italy ⊥ Department of Health Sciences, Faculty of Medicine and Surgery, University of Molise, Campobasso, Italy ‡

ABSTRACT: Nuclear magnetic resonance (NMR)-based metabolomics separates exhaled breath condensate (EBC) profiles of patients affected by pulmonary disease from those of healthy subjects. Here we show the discriminatory ability of NMR-based metabolomics in separating patients exposed to the same risk factor, namely, smoking habit in smoking-related diseases. Fifty duplicated EBC samples from a cohort of current smokers without chronic obstructive pulmonary disease (COPD, henceforth HS), COPD smokers, and subjects with established pulmonary Langerhans cell histiocytosis (PLCH) were analyzed by means of NMR spectroscopy followed by principal component analysis (PCA) and projection to latent structures discriminant analysis (PLSDA). Clusterization of EBC spectra was disease-specific. COPD and PLCH samples present a profile different from that of HS, showing acetate increase and 1-methylimidazole reduction. An inverse behavior of 2-propanol and isobutyrate characterized COPD with respect to PLCH (high/low in COPD, low/high in PLCH). Both the 2-component and the 3-component PLS-DA models showed a 96% cross-validated accuracy, presenting R2 and Q2 values in the ranges of 0.97−0.87 and 0.91−0.78, respectively, and R2 = 0.87 and Q2 = 0.78, indicating that data variation is well explained by each model (R2), with a good predictivity (Q2). NMR spectra of EBC discriminate COPD and PLCH patients from HS and between them, with well-defined metabolic profiles for each class. The specificity of EBC profiles suggests that disease itself drives metabolic separation overwhelming the “common background” due to smoking habit. EBC-NMR investigation offers a powerful tool for assessing the evolution of airway diseases even in the presence of a strong common factor. KEYWORDS: exhaled breath condensate, NMR, metabolomics, COPD, histiocytosis

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children,4 as well as patients with stable and unstable cystic fibrosis.5 For EBC studies, NMR spectroscopy presents several advantages. It requires minimal sample preparation with rapid acquisition time of spectra (10−15 min), with a fairly high degree of sensitivity (we reported a detection limit of 0.14 ± 0.04 μM).5 NMR is also able to detect potential saliva contamination of EBC, as well as the effect of external contaminants, all crucial for a correct analysis of biomarkers variability.6 Since NMR spectra present several resonances, discriminating elements in a series of spectra are detected by resorting to multivariate analysis, which carefully identifies hidden phenomena and trends in ensembles of spectra.7 The most often used statistical techniques are principal component

INTRODUCTION Exhaled breath condensate (EBC) is a suitable matrix for analyzing airway inflammation and oxidative stress biomarkers.1 It is a noninvasive method for sampling airway secretions, acceptable to patients and potentially useful for longitudinal studies.1 Metabolomics is “the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” due to any exposure.2 It has potential application in early diagnosis and disease phenotyping and may also identify unexpected or even unknown metabolites favoring new pathophysiological hypotheses. It has been demonstrated that EBC can successfully be studied by nuclear magnetic resonance (NMR) spectroscopy and that metabolite markers separate adult patients affected by chronic obstructive pulmonary disease (COPD) from laryngectomized subjects and healthy controls3 and identify asthmatic © 2013 American Chemical Society

Received: December 13, 2012 Published: January 29, 2013 1502

dx.doi.org/10.1021/pr301171p | J. Proteome Res. 2013, 12, 1502−1511

Journal of Proteome Research

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Table 1. Anthropometric Characteristics of 20 Control Subjects (HS), 15 Patients Affected by COPD, and 15 Patients Affected by PLCHa subjects

sex (M/F)

age (yr)

BMI (kg/m2)

FEV1 (L)

FEV1 (% pred)

FVC (L)

FEV1/FVC (%)

tobacco exp (pk/yr)

20 HS 15 COPD 15 PLCH

8/12 11/4 9/6

41.9 ± 12.9 66.9 ± 9.9 34.2 ± 7.5

26.7 ± 3.5 26.5 ± 3.4 26.4 ± 2.9

3.0 ± 0.4 1.7 ± 0.4 2.8 ± 0.6

95.7 ± 5.5 66.1 ± 13.6 82.8 ± 7.3

3.7 ± 0.6 3.00 ± 0.8 3.6 ± 0.6

81.2 ± 4.4 56.5 ± 11.5 78.1 ± 5.0

14.7 ± 5.5 16.4 ± 5.1 14.3 ± 4.5

Values are expressed as mean ± SD. M: male. F: female. BMI: body mass index. FEV1: forced expiratory volume in 1 s. % pred: % of predicted value. FVC: forced vital capacity. FEV1, FVC, and FEV1/FVC were measured after bronchodilatation inhalation test. GOLD stage classification was as follows: stage I, 1patient; stage II, 12 patients; stage III, 2 patients; stage IV, none. Tobacco exposure was estimated in packs/year by asking the subjects the number of cigarettes/day, multiplied for the past 10 years. a

years, FEV1 % pred mean = 94.9 ± 6.5%) were analyzed by means of NMR spectroscopy followed by principal component analysis (PCA) and projection to latent structures discriminant analysis (PLS-DA). Anthropometrics, demographics data of subjects herein investigated, are reported in Table 1. All HS controls were current smokers with no spirometric alterations and no history of respiratory or other diseases. They had no upper airway infections nor had received any medication in the previous 4 weeks. COPD patients, representing mild to moderate disease stages, received diagnosis in the past according to Global Obstructive Lung Disease (GOLD) guidelines,20 with the following stage classification: stage I, 1 patient; stage II, 12 patients; stage III, 2 patients; stage IV, none. Patients had postbronchodilator FEV1/FVC < 70% and FEV1 < 80% of the predicted value. All COPD patients were treated using regular inhaled combined therapy with β2 agonists plus corticosteroids, inhaled β2 agonists, or anticholinergic agents and were asked not to use inhaled therapy for at least 12 h before EBC collection. PLCH patients received histological and/or radiological diagnosis by pathologic (9 patients) specimens or characteristic chest CT scan findings (6 patients) as previously described.16 Histological diagnosis was determined on pulmonary biopsy specimen obtained by both trans-bronchial fiberoscopy or video-assisted thoracic scopy (VATS). All 15 PLCH patients had a history of smoking, with 14 being current smokers at time of evaluation and 1 former smoker from less than 1 month. All subjects presented no occupational or other pronounced exposure to organic solvents and were free from upper and/or lower airway infection from at least 4 weeks before the EBC collection. Exclusion criteria were a history of medical illnesses (heart failure, long-standing diabetes, liver, kidney, metabolic, hematologic, gastrointestinal, neurologic), abnormalities on screening physical exam or by laboratory testing (chemistry profile, complete blood count), >120% ideal body weight, or change in body weight >5 kg over the preceding 6 months. HS control, COPD, and PLCH patients were recruited from patients of Monaldi Hospital, Naples, Italy, from 15 January 2010 to 15 July 2010. All subjects gave informed consent, and the study protocol was approved by the Ethics Committee of the Monaldi Hospital.

analysis (PCA) and soft independent modeling of class analogy (SIMCA), which build local models for class membership and identification purposes and are potentially useful for subphenotyping. Smoking-induced lung diseases constitute a complex group of respiratory disorders characterized by a strict association between tobacco exposure and parenchymal pulmonary damage, varying from COPD to interstitial lung diseases (ILDs), the latter group comprising pulmonary Langerhans cell histiocytosis (PLCH).8 COPD is a progressive pulmonary disease characterized by airflow limitation not fully reversible,9 with active smoking as a major risk factor. Although individual susceptibility to tobacco smoke effects can interact synergistically with other risk factors,10 smokers without COPD (HS) present an absolute risk of developing COPD ranging between 25% and 50%.11−13 Previous findings suggest that the inflammatory effects of current smoking may mask the underlying ongoing inflammatory process of COPD14 as smoking causes an acute oxidative burst in the airways of HS,15 and therefore the rise of COPD can be obscured by reaction of the adaptive immune system to smoke exposure. PLCH is a rare and essentially sporadic granulomatous disorder characterized by uncontrolled proliferation and infiltration of TCD1+ (Langerhans cells) in the lung, forming multiple, bilateral, interstitial, peribronchiolar nodules. The bronchiolar distribution of lesions suggests involvement of cigarette smoke since 90% of cases are smokers.16 Furthermore, acute tobacco smoke inhalation determines immediate and selective recruitment of Langerhans cells into human airways, inducing a very early reaction of the adaptive immune system.14 A proteomic approach to PLCH bronchoalveolar lavage (BAL) indicates that smoke-induced alterations determine a typical protein profile at bronchoalveolar and plasma levels.15 EBC has been used to study the influence of smoking on airway epithelium and its changes in smoking-related airways diseases,17−19 but there are no data on metabolic variation in different diseases sharing smoking as a risk factor. The aim of this study was to explore the ability of NMRbased metabolomics in discriminating different EBC profiles of patients exposed to the same risk factor, such as a smoking habit, eventually identifying metabolic patterns specific for each group of subjects.

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EBC Sampling

MATERIALS AND METHODS

EBC was collected [an average of 2.0 ± 0.2 mL (mean ± SD) of EBC in 20 min] using a condensing chamber (Ecoscreen, Jaeger, Hoechberg, Germany) as previously reported.3 Subjects were asked to refrain from food intake and drinking alcohol at least 8 and 18 h, respectively, before EBC collection and refrain from smoking for at least 24 h. Briefly, subjects were asked to breathe tidally through a mouthpiece into a two-way nonrebreathing valve, which also served as a saliva trap, for 15 min

Subjects

In total 50 EBC duplicate samples (n = 100) obtained from 15 patients with confirmed (histological and/or radiological) diagnosis of PLCH (6F/9M, mean age 34.2 ± 7.5 years), 15 current smokers COPD patients (4F/11M, mean age 66.9 ± 9.0 years, FEV1 % pred mean = 66.1 ± 13.6%), and 20 current smokers without COPD (HS, 12F/8M, mean age 46.2 ± 11.7 1503



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referred to the lactate doublet (δCH3) resonating at 1.33 ppm for 1H and 20.76 ppm for 13C.

wearing a nose-clip. EBC samples were immediately transferred into 10 mL glass vials, closed with 20 mm butyl rubber lined with PTFE septa, and crimped with perforated aluminum seals. Before sealing, volatile substances were removed from samples by a gentle nitrogen gas flow for 3 min.3,6 Samples were not dried out to avoid precipitation with a possible loss of nonvolatile compounds and/or formation of insoluble aggregates upon dissolving the dried condensate for NMR measurements. Sealed samples were then frozen in liquid nitrogen, so as to immediately “quench” metabolism and preserve the metabolites’ concentrations. Samples were stored at −80 °C until spectra acquisition. α-Amylase activity was determined with a highly sensitive assay kit (detection limit 2 U/L) to exclude salivary contamination (EnzCheck Ultra Amylase Assay Kit, Invitrogen, Paisley, U.K.). None of the EBC samples contained detectable levels of amylase activity. Before and after collection of each EBC sample, the reusable parts of the EcoScreen condenser were soaked for 15 min with a sodium hypochlorite solution (3.55 mM Milton solution, according to manufacturer guidelines) and then flushed for 15 min with deionized distilled water and dried.6

Spectral and Statistical Analysis

Before analysis, we assessed within-day, between-day, and technical repeatabilities and detection limit following the procedure reported.5 Within-day and technical repeatabilities were assessed by considering all of the peaks detected in the NMR spectrum, while the 4.4−0.4 ppm spectral region was used for assessing between-day repeatability. For within-day repeatability, two EBC samples were collected for each subject within the same day (at times 0 and 8 h) for a total of 16 subjects (5 smokers without COPD, 5 COPD patients, and 6 PLCH patients). Each spectrum was subdivided into 6 regions (region 1: 8.6−6.6 ppm; region 2: 6.6−5.2 ppm; region 3: 4.4−3.4 ppm; region 4: 3.4−2.4 ppm; region 5: 2.4− 1.4 ppm; region 6: 1.4−0.4 ppm), while region 5.2−4.4 ppm, containing the residual water resonance, was excluded. All regions were integrated and normalized to the total spectrum area to avoid possible variation of metabolite concentrations due to differences in volume during EBC collection, as previously described.4 We obtained 6 parameters (the integrated fractional regions) for each spectrum, which for 16 selected subjects (spectra) gave 96 total values. The SD was within ±1.96 SD in 95 out of 96 samples, which according to the Bland−Altman test indicates good within-day repeatability. For assessing between-day repeatability, which was expressed as intraclass correlation coefficient (ICC), three EBC samples, collected from the above 16 subjects on days 1, 3, and 7, were analyzed with NMR spectroscopy. The 4.4−0.4 ppm spectral area was integrated and normalized to the total spectrum area. The determined ICC was 0.97. Technical repeatability was assessed by repeating NMR spectroscopy on three different samples (1 from smoker without COPD, 1 from a COPD patient, and 1 from a PLCH patient) 10 times consecutively. The ICC for the 4.4−0.4 ppm spectral region was 0.98. The detection limit of our NMR technique was calculated by integrating 10 EBC spectra; they were normalized to the standard TSP (0.1 mM) internal reference, obtaining an average concentration of 0.13 ± 0.03 μM for the endogenous phenylalanine (signals at 7.3−7.0 ppm), among the lowest detected signals. High-resolution 1H NMR spectra were automatically data reduced to 400 integral segments (“buckets”), each of 0.02 ppm, using the AMIX 3.6 software package (Bruker Biospin GmbH, Rheinstetten, Germany) between 0.12−8.60 ppm spectral region. The residual water resonance region (4.40− 5.20 ppm) was excluded, and each integrated region was normalized to the total spectrum area to avoid possible signal variation due to dilution of EBC samples, which could result in an artificial increase of the signals. To discriminate different EBC samples through NMR spectra, we carried out a multivariate statistical data analysis using projection methods. The integrated data reduced format of the spectra was imported into SIMCA-P+ 12 package (Umetrics, Umeå, Sweden) and preprocessed with Pareto scaling. Each spectral region was scaled to (1/sk)1/2, where sk is the standard deviation for variable k, increasing the contribution of lower concentration metabolites in the generated models compared with models where no scaling is used. In this case, all variables are given equal weighting, so that the model is not biased toward the higher magnitude variables

NMR Sample Preparation

EBC samples were rapidly defrosted. To provide a field frequency lock, 70 μL of a 2H2O solution [containing 1 mM sodium 3-trimethylsilyl [2,2,3,3-2H4] propionate (TSP) as a chemical shift reference for 1H spectra and sodium azide at 3 mM] was added to 630 μL of EBC to give 700 μL of total volume. NMR Spectroscopy Measurements

All spectra were recorded on a 600 MHz Bruker Avance III spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany) equipped with a CryoProbe. One-dimensional (1D) 1H NMR spectra were collected at 300 K with the excitation sculpting pulse sequence21 to suppress the water resonance. We used a double-pulsed field gradient echo, with a soft square pulse of 4 ms at the water resonance frequency, with the gradient pulses of 1 ms each in duration, adding 128 transients of 64k complex points, with an acquisition time of 4 s/transient. Time-domain data were all zero-filled to 128k complex points, and prior to Fourier transformation, an exponential multiplication of 0.6 Hz was applied. Two-dimensional (2D) clean total correlation spectroscopy (TOCSY)22 spectra were recorded using a standard pulse sequence and incorporating the excitation sculpting sequence for water suppression. In general, 320 equally spaced evolution-time period t1 values were acquired, averaging 4 transients of 2048 points. Time-domain data matrices were all zero-filled to 4096 points in both dimensions, and prior to Fourier transformation, a Lorentz-to-Gauss window with different parameters was applied for both t1 and t2 dimensions for all of the experiments. Both 1D and 2D spectra were referred to 0.1 mM TSP, assumed to resonate at δ = 0.00 ppm. For the natural abundance 2D 1H−13C heteronuclear single quantum coherence (HSQC) spectra, we used an echoantiecho phase sensitive pulse sequence using adiabatic pulses for decoupling.23 One hundred and twenty-eight equally spaced evolution time period t1 values were acquired, averaging 48 transients of 2048 points and using GARP4 for decoupling. The final data matrix was zero-filled to 4096 in both dimensions and apodized before Fourier transformation by a shifted cosine window function in t2 and in t1. Linear prediction was also applied to extend the data to twice its length in t1. Spectra were 1504



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Figure 1. NMR spectra of EBC samples. Representative one-dimensional 1H spectra of (A) a smoker without COPD control, (B) a patient with COPD, and (C) a patient with PLCH. The 9.0−6.5 ppm region has been vertically expanded 32 times with respect to the 4.5−0.5 ppm region.

(as these generally have larger variances). PCA was first applied to detect EBC metabolites trends and clusterings in an unsupervised (i.e., no prior group knowledge is used in the calculation) manner. However, to better identify clusterings, the spectral filtering orthogonal signal correction (OSC) routine24 together with PLS-DA were applied to reinforce classification. By using the OSC filter, we obtained regression models involving all spectral data (HS subjects, COPD, and PLCH) and the possible permutation within the three classes (HS and COPD, HS and PLCH, and COPD and PLCH). For the three pairs, HS and COPD were used as the onecomponent Y matrix. Variations orthogonal to the Y vector representing the HS and COPD state were subtracted to produce a data set that is more influenced by the metabolomic analysis of EBC. In addition, for each filter a permutation test (n = 300) was carried out to test possible overfit of the model. The final data were examined applying PLS-DA to discriminate between smoker without COPD subjects and patients and between each class in the three pairs. Data were expressed as mean ± SD after assessing for normality with the D’Agostino−Pearson omnibus normality test. One-way ANOVA and unpaired t test were used for comparing groups. Significance was defined as a value of p < 0.05.

6.0 ppm,3 are absent in the EBC spectra of Figure 1. To examine trends and clusterings of EBC classes, we first applied unsupervised PCA to HS vs COPD, HS vs PLCH, and COPD vs PLCH patients. A sample classification of 66% (samples classes correctly identified) was obtained (data not shown). We next applied PLS-DA with the OSC spectral filtering routine, obtaining a stronger regression model (96%, p < 0.0001) between HS and COPD (Figure 2A), HS and PLCH (Figure 2B), and COPD and PLCH (Figure 2C). The resulting supervised models were tested by iteratively predicting the class membership of every sample, and the results were used to evaluate the goodness of fit (R2) and the goodness of prediction (Q2), for which acceptable values must be ≥0.5.27 For the model describing HS vs COPD (Figure 2A) we recorded R2 = 0.97 and Q2 = 0.91; for HS vs PLCH we obtained R2 = 0.87 and Q2 = 0.79; and for COPD vs PLCH subjects we recorded R2 = 0.90 and Q2 = 0.81. When the EBC NMR profiles of all three groups (COPD, PHLC, and HS) were analyzed in one model, it showed a cross validated accuracy value of 96% (p < 0.0001). The OSC-filtered PLS-DA statistical analysis of data yielded a model for the three classes, which resulted in three predictive and three orthogonal components (Figure 3), with R2 = 0.87 and Q2 = 0.78, indicating that data variation is well explained by the model (R2), with a very good predictivity (Q2). Therefore, each group of patients with respiratory disease (COPD or PLCH) separated from the other and both from the HS group. Since the COPD and PLCH patients show different age ranges (58−78 and 29−49 years, with mean ± SD of 66.9 ± 9.0 and 34.2 ± 7.5 years, respectively, Table 1), we evaluated the possibility that age could be a potential differentiating factor of classes. We therefore divided the HS class in two age-related subgroups (with 40 years as an arbitrary cutoff), corresponding to a mean ± SD of 30.1 ± 3.8 and 52.3 ± 7.8 years, respectively. Their NMR spectra are compared in Figure 4, which shows representative 1D spectra of EBC of patients in the 40 years range

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RESULTS The EBC NMR spectra of COPD and PLCH patients were compared to those of HS subjects. Figure 1 depicts representative EBC 1D spectra of a HS (Figure 1A), a COPD patient (Figure 1B), and a PLCH patient (Figure 1C). Spectral lines were assigned to specific metabolites by resorting to 2D 1H−1H TOCSY and 1H−13C HSQC experiments (not shown) with the aid of the Human Metabolome Database25 and published chemical-shift data on metabolites.26 No saliva contamination was detected as the most intense saliva signals, originating from carbohydrates and resonating between 3.3 and 1505



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Figure 3. PLS-DA with OSC spectral filtering of EBC samples of HS subjects (blue squares) compared with COPD (black triangles) and PLCH (red circles) patients. The model showed a cross validated accuracy value of 96%, with R2 = 0.87 and Q2 = 0.78.

robust model should have R2, Q2 ≥ 0.5, and |R2 − Q2| < 0.2− 0.3,27 we conclude that age does not affect the interpretation and the statistical analysis of the heavy smokers samples. The variables responsible for between-class differences observed in the score plots of Figures 2 and 3 can be identified from loading plots corresponding to HS and COPD, HS and PLCH, COPD and PLCH, and HS, COPD, and PLCH subjects (not shown). The most relevant metabolites are depicted in the variable of importance plots (VIPs) in Figure 5. It is important to notice that when more than one chemical group belonging to the same metabolite is observable in the NMR spectrum, that metabolite is reported more than once on VIPs. For example, two ethanol buckets at 1.19 and 3.67 ppm are reported in the panels of Figure 5, corresponding to the methyl and the methylene groups, respectively. Not all of the found metabolites are required for betweengroup classification. Although the acetate (at 1.93 ppm) is the most important metabolite in all models considered, the sequence of buckets depicted in the panels of Figure 5 varies. In order to reduce the number of buckets but obtain reliable models with only 10% reduction of the quality parameters R2 and Q2, we constantly reduced the number of metabolites, obtaining a best-fit with the first 9 metabolites (corresponding to 15 buckets), namely, acetate (bucket at 1.93 ppm), acetoin (1.37 and 2.23 ppm; the signal at 2.23 ppm is partially superimposed with acetone), ethanol (1.19 and 3.65 ppm), formate (8.45 ppm), methanol (3.37 ppm), 1-methylimidazole (3.71 ppm), 2-propanol (1.18 ppm), propionate (1.06 and 2.19 ppm), and isobutyrate (1.15 and 2.36 ppm). Interestingly, the 9 identified metabolites are common to all subgroups but separate them because of their different concentration in each class. Figure 6 reports the concentration variation of single metabolite in the different classes; the corresponding concentrations, obtained averaging the signals in all spectra for each subgroup, are reported in Table 2. Specific trends can be identified: acetate, ethanol, methanol, propionate, and to a

Figure 2. Partial least-squares-discriminant analysis (PLS-DA) with orthogonal signal correction (OSC) spectral filtering of EBC of COPD, PLCH, and smoker without COPD (HS) subjects. Score plots showing the degree of separation of the model between (A) HS subjects (filled squares) and COPD patients (filled triangles), (B) HS subjects (filled squares) and PLCH patients (filled circles), and (C) COPD (filled triangles) and PLCH (filled circles) patients. All of the models showed a strong regression (96%), obtaining the following quality parameters: (A) R2 = 0.97 and Q2 = 0.91, (B) R2 = 0.87 and Q2 = 0.75, and (C) R2 = 0.90 and Q2 = 0.81. The labels t[1] and t[2] along the axes represent the scores (the first two partial least-squares components) of the model, which are sufficient to build a satisfactory classification model.

(Figure 4B). All spectral signals were identified and assigned to specific metabolites as above. Trends and clusterings of EBC samples were first investigated by unsupervised PCA, obtaining a sample classification of 14% (samples’ classes correctly identified) (data not shown). We then applied PLS-DA with the OSC spectral filtering routine: components 1 (t[1]) and 2 (t[2]) in the score plots of Figure 4C were unable to build a satisfactory classification model, obtaining R2 = 0.18, Q2 = 0.15. Since a 1506



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Figure 4. NMR spectra and partial least-squares-discriminant analysis (PLS-DA) of EBC samples from smoker without COPD (HS) reference group. Representative one-dimensional 1H spectra of a HS control from (A) the 40 years range. (C) PLS-DA with OSC spectral filtering of the two HS age-related subgroups: empty circles, 40 range. The corresponding mean ± SD was 30.1 ± 3.8 and 52.3 ± 7.8 years, respectively. No satisfactory classification model was obtained, with R2 = 0.18 and Q2 = 0.15.

respect to HS subjects. Furthermore, the method unambiguously recognizes metabolites responsible for between-group differences, strongly suggesting that the biomarker signature characterizing PLCH and COPD is independent from the “common background” due to smoking habit. Furthermore, a limited number of metabolites identifies molecular changes in smoking-related diseases such as COPD and PLCH. We uncovered acetate, acetoin, ethanol, formate, methanol, 1-methylimidazole, 2-propanol, propionate, and isobutyrate as responsible for intergroup separation and, on the basis of their different concentrations, identified specific trends. In particular (Figure 6), high acetate characterizes COPD and PLCH, representing a key metabolite to differentiate them from HS. In parallel, COPD and PLCH present a reduction of 1methylimidazole with respect to smokers without COPD, while COPD and PLCH can be differentiated by high 2propanol and low isobutyrate (COPD) and high isobutyrate and low 2-propanol (PLCH). Although speculative, a ration-

lesser extent 2-propanol show a steady increase on going from HS to COPD and to slightly decrease on going from COPD to PLCH, except for methanol that shows a slight increase (Table 2 and Figure 6). On the contrary, formate, 1-methylimidazole, acetoin, and isobutyrate decrease in COPD and increase from COPD to PLCH (Table 2 and Figure 6). Figure 6 highlights that high acetate level characterizes COPD and PLCH, representing a key metabolite to differentiate them from HS group. In parallel, COPD and PLCH present a reduction of 1-methylimidazole with respect to smokers, while COPD and PLCH can be differentiated by high 2-propanol and low isobutyrate (COPD) and high isobutyrate and low 2-propanol (PLCH).

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DISCUSSION We have demonstrated that COPD EBC samples present an NMR profile different from that of PLCH samples and that NMR is able to unequivocally identify each of them with 1507



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Figure 5. Variables of importance plots (VIPs) representing the most important “buckets” generating the models reported in Figures 2 and 3. (A) Smoker without COPD (HS) controls vs COPD patients (R2 = 0.97 and Q2 = 0.91), (B) HS controls vs PLCH patients (R2 = 0.87 and Q2 = 0.75), (C) COPD vs PLCH patients (R2 = 0.90 and Q2 = 0.81), and (D) COPD and PLCH patients vs HS controls (R2 = 0.87 and Q2 = 0.78). The x-axis reports the buckets, identified with chemical shift (in ppm), and is labeled “VAR-ID” (variable identity); the y-axis, labeled “VIP[2]”, shows the strength of the labeled metabolites in the classification between/among subject classes. Error bars represent 95% CIs.

pathways in the mitochondria, forming two acetate molecules for each butyrate molecule,35 it is conceivable that the six- and 5-fold acetate increase, respectively, in COPD and in PLCH, could be due to butyrate β-oxidation, which is reported to increase in smoke-exposed A549 cells.28 Endogenous formation of 2-propanol can occur in humans, possibly through the same pathway observed in rats, where it forms from reduction of acetone by liver alcohol dehydrogenase, mainly when high levels of acetone and high NADH/ NAD+ ratios occur,36,37 as observed in ketosis. The highest 2propanol concentration was observed in COPD (Table 2), but we did not detect acetone variation, and none of the patients presented metabolic ketosis, ketoacidosis, or respiratory acidosis, although to the best of our knowledge, no acetone had been reported in respiratory acidosis, usually caused by impaired breathing as in COPD.38 Isobutyrate arises from valine catabolism.39 Compared with HS, we observed a 3-fold decrease in COPD and a 5-fold increase in PLCH, which implies a 15-fold increase on going from COPD to PLCH (Table 2). Interestingly, patients with inflammatory bowel disease (IBD) produce more isobutyric and isovaleric acids than healthy controls.40 IBD is also associated with pulmonary manifestations,41 and a relationship between IBD and PLCH has been reported.42 The reason why isobutyrate behavior is different in COPD and PLCH is not clear, but it could favor a noninvasive monitoring of both pathologies.

alization of discriminatory metabolite increase/decrease can be hypothesized. The acetate increase observed in COPD and PLCH could be linked to cholesterol increase since, as shown in the mevalonate pathway, the cholesterol molecule is formed from acetate units. High cholesterol level has been reported in A549 human alveolar epithelial carcinoma cells exposed to cigarette smoke, most likely through increased fatty acid β-oxidation.28 Considering that a flux of exogenous acetate to acetylcarnitine has been observed in C57BL/6 mice29 and that the carnitine system is mainly involved in the transport of fatty acids into cellular mitochondria for their conversion into energy, the increased acetate concentration may relate to energy needs. In line with acetate increase, the sharp propionate rise also suggests involvement of lipid metabolism. In fact, since increased production of propionate seems to inhibit cholesterol synthesis, it can be related to higher lipolysis, leading to elevated serum free fatty acids associated with smoking.30 A possible anti-inflammatory action for propionate can also be put forward. Short-chain fatty acids regulate several leukocyte functions, including production of cytokines, eicosanoids, and chemokines, and seem to affect leukocytes migration to the foci of inflammation.31,32 We observed acetate and propionate increase in COPD and PLCH with respect to HS, but no butyrate was detected. Propionate certainly shares many butyrate actions;33,34 however, since butyrate is oxidized through the fatty acid β-oxidation and tricarboxylic acid cycle 1508



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Figure 6. Concentration distribution of the most important nine metabolites separating smoker without COPD controls and COPD and PLCH patients. (A, B) Histograms show metabolites’ concentration (μM) in the three EBC classes. Metabolites are identified by a color code and a number corresponding to the labeling of Table 2. (C, D) Graphs show the corresponding concentration (μM) variation of the metabolites in the three classes, each labeled as in Table 2.

Table 2. Concentrations of EBC Metabolites Responsible for between-Group Classificationa metabolite concentration (μM) subjects

1, ethanol

2, methanol

3, acetate

4, propionate

5, formate

6, 1-methyl imidazole

7, acetoinb

8, 2-propanol

9, isobutyrate

20 HS 15 COPD 15 PLCH

0.95 ± 0.35 6.54 ± 2.21

2.66 ± 1.02 7.96 ± 2.39

5.48 ± 1.91 30.50 ± 12.51

1.32 ± 0.47 9.51 ± 4.38

1.53 ± 0.51 0.39 ± 0.12

4.49 ± 1.95 0.22 ± 0.084

4.19 ± 1.47 2.16 ± 0.72

0.030 ± 0.011 4.11 ± 1.60

1.17 ± 0.42 0.41 ± 0.17

4.68 ± 2.02

8.57 ± 3.35

24.52 ± 10.19

6.51 ± 2.81

1.31 ± 0.52

1.27 ± 0.53

3.29 ± 1.29

0.54 ± 0.24

6.09 ± 2.63

NMR signals in each class were integrated and referred to the final TSP signal of known concentration (100 μM). EBC metabolite concentrations are expressed as mean ± SD. One-way ANOVA was used for comparing groups. Labeling identifies metabolites as in Figure 6. bThe signal at 2.23 ppm partially overlaps with acetone. a

by high 2-propanol and low isobutyrate (COPD) and high isobutyrate and low 2-propanol (PLCH).

The concentration of 1-methylimidazole, identified through a characteristic singlet at 3.71 ppm, shows a 20-fold reduction in COPD and ca. 3-fold reduction in PLCH, which implies a 6fold increase in PLCH with respect to COPD (Table 2). Although the whole molecule is unidentified, we believe that it could be 1-methylimidazole-4-acetic acid, which originates from histamine catabolism and is significantly high in smokers’ urinary excretion.43 It is interesting to notice that, on going from HS, 1-methylimidazole concentration is drastically reduced in COPD and in PLCH with a ratio of 1:0.05:0.28, respectively. Therefore, COPD and PLCH can be discriminated

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CONCLUSIONS The above metabolites strongly define two different “metabotypes” for COPD and PLCH. They also indicate that the common smoking habit/chronic exposure does not determine a “metabolic homogeneity” and that NMR-based metabolomics is able to clearly characterize the specific boundaries of airway respiratory diseases through EBC. This is very important since NMR profiling makes no a priori assumptions about EBC 1509



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biological NMR spectroscopic data. Xenobiotica 1999, 29 (11), 1181−9. (3) de Laurentiis, G.; Paris, D.; Melck, D.; Maniscalco, M.; Marsico, S.; Corso, G.; Motta, A.; Sofia, M. Metabonomic analysis of exhaled breath condensate in adults by nuclear magnetic resonance spectroscopy. Eur. Respir. J. 2008, 32 (5), 1175−83. (4) Carraro, S.; Rezzi, S.; Reniero, F.; Héberger, K.; Giordano, G.; Zanconato, S.; Guillou, C.; Baraldi, E. Metabolomics applied to exhaled breath condensate in childhood asthma. Am. J. Respir. Crit. Care Med. 2008, 175 (10), 986−90. (5) Montuschi, P.; Paris, D.; Melck, D.; Lucidi, V.; Ciabattoni, G.; Raia, V.; Calabrese, C.; Bush, A.; Barnes, P. J.; Motta, A. NMR metabolomic profiling of exhaled breath condensate in patients with stable and unstable cystic fibrosis. Thorax 2012, 67 (3), 222−8. (6) Motta, A.; Paris, D.; Melck, D.; de Laurentiis, P.; Maniscalco, M.; Sofia, M.; Montuschi, P. NMR-based metabolomics of exhaled breath condensate: methodological aspects. Eur. Respir. J. 2012, 39 (2), 498− 500. (7) Lindon, J. C.; Holmes, E.; Nicholson, J. K. Pattern recognition methods and applications in biomedical magnetic resonance. Prog. Nucl. Magn. Reson. Spectrosc. 2001, 39 (1), 1−40. (8) Caminati, A.; Harari, S. Smoking-related interstitial pneumonias and pulmonary Langerhans cell histiocytosis. Proc. Am. Thorac. Soc. 2006, 3 (4), 299−306. (9) Rabe, K. F.; Hurd, S.; Anzueto, A.; Barnes, P. J.; Buist, S. A.; Calverley, P.; Fukuchi, Y.; Jenkins, C.; Rodriguez-Roisin, R.; van Weel, C.; Zielinski, J. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am. J. Respir. Crit. Care Med. 2007, 176 (6), 532−55. (10) Marsh, S.; Aldington, S.; Shirtcliffe, P.; Weatherall, M.; Beasley, R. Smoking and COPD: what really are the risks? Eur. Respir. J. 2006, 28 (4), 883−4. (11) Lundbäck, B.; Lindberg, A.; Lindström, M.; Rönmark, E.; Jonsson, A. C.; Jönsson, E.; Larsson, L. G.; Andersson, S.; Sandström, T.; Larsson, K. Obstructive Lung Disease in Northern Sweden Studies. Not 15 but 50% of smokers develop COPD? Report from the Obstructive Lung Disease in Northern Sweden Studies. Respir. Med. 2003, 97 (2), 115−22. (12) Rennard, S. I.; Vestbo, J. COPD: the dangerous underestimate of 15%. Lancet 2006, 367 (9518), 1216−9. (13) Løkke, A.; Lange, P.; Scharling, H.; Fabricius, P.; Vestbo, J. Developing COPD: a 25 year follow up study of the general population. Thorax 2006, 61 (11), 935−9. (14) Lommatzsch, M.; Bratke, K.; Knappe, T.; Bier, A.; Dreschler, K.; Kuepper, M.; Stoll, P.; Julius, P.; Virchow, J. C. Acute effects of tobacco smoke on human airway dendritic cells in vivo. Eur. Respir. J. 2010, 35 (5), 1130−6. (15) Landi, C.; Bargagli, E.; Magi, B.; Prasse, A.; Muller-Quernheim, J.; Bini, L.; Rottoli, P. Proteome analysis of bronchoalveolar lavage in pulmonary Langerhans cell histiocytosis. J. Clin. Bioinforma. 2011, 1, 31 DOI: 10.1186/2043-9113-1-31. (16) Rao, R. N.; Goodman, L. R.; Tomashefski, J. F., Jr. Smokingrelated interstitial lung disease. Ann. Diagn. Pathol. 2008, 12 (6), 445− 57. (17) Corradi, M.; Rubinstein, I.; Andreoli, R.; Manini, P.; Caglieri, A.; Poli, D.; Alinovi, R.; Mutti, A. Aldehydes in exhaled breath condensate of patients with chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2003, 167 (10), 1380−1386. (18) Garey, K. W.; Neuhauser, M. M.; Robbins, R. A.; Danziger, L. H.; Rubinstein, I. Markers of inflammation in exhaled breath condensate of young healthy smokers. Chest 2004, 125 (1), 22−6. (19) Koczulla, A. R.; Noeske, S.; Herr, C.; Jörres, R. A.; Römmelt, H.; Vogelmeier, C.; Bals, R. Acute and chronic effects of smoking on inflammation markers in exhaled breath condensate in current smokers. Respiration 2010, 79 (1), 61−7. (20) American Thoracic Society. Standardization of spirometry: 1994 update. Am. J. Respir. Crit. Care Med. 1995, 152 (3), 1107−36.

components that may be associated with a particular disease. Furthermore, it analyzes the sample in a multiparametric way, identifying new and unsuspected links between processes and pathways perturbed in a disease state. This could afford a more efficient diagnosis and treatment even in the presence of a common phenotypic background. In fact, COPD and PLCH profiles are not masked by the common tobacco exposure, and patients are clearly separated from subjects without respiratory diseases, both qualitatively and quantitatively. This approach allows identification of unbiased potential biomarkers of airway disease, some of which may be useful in clinical trials, and can also unravel metabolic changes due pharmacological treatment. This may be a valuable contribution to the definition and management of airway diseases, although longitudinal studies are required to confirm this. We are aware that the present study relies on a limited number of subjects and that the small sample size did not permit an external validation in an independent group of subjects. Our data are, therefore, preliminary, and further studies are needed to determine prospectively, in a separate group of patients, the relevance of the model in defining the boundaries of smoking related diseases. However, we confirmed that NMR-based metabolomics consolidates ‘‘breathomics’’ as a new noninvasive holistic approach for the assessment of respiratory diseases, with important diagnostic and therapeutic implications.

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AUTHOR INFORMATION

Corresponding Author

*Ph: +39 081 867 5228/5226. Fax: +39 081 804 1770. E-mail: [email protected]. Author Contributions ¶

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Matteo Stocchero, S-IN Soluzioni Informatiche (Vicenza, Italy) for helpful discussions on the merits of PLSDA. We are grateful to patients for their important contribution to this study. This research was supported by funds from CNR.

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ABBREVIATIONS COPD, chronic obstructive pulmonary disease; EBC, exhaled breath condensate; PLCH, pulmonary Langerhans cell histiocytosis

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REFERENCES

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Separating Smoking-Related Diseases Using NMR-Based

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