1H NMR To Explore the Metabolome of Exhaled Breath Condensate

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1H-NMR TO EXPLORE THE METABOLOME OF EXHALED BREATH CONDENSATE IN #1-ANTITRYPSIN DEFICIENT PATIENTS: A PILOT STUDY Cristina Airoldi, Carlotta Ciaramelli, Marco Fumagalli, Rita Bussei, Valeria Mazzoni, Simona Viglio, Paolo Iadarola, and Jan Stolk J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00648 • Publication Date (Web): 20 Sep 2016 Downloaded from http://pubs.acs.org on September 21, 2016

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Journal of Proteome Research

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H-NMR TO EXPLORE THE METABOLOME OF EXHALED BREATH CONDENSATE IN α1-ANTITRYPSIN DEFICIENT PATIENTS: A PILOT STUDY Cristina Airoldi,*a Carlotta Ciaramelli,a Marco Fumagalli,b Rita Bussei,a Valeria Mazzoni,a Simona Viglio,c Paolo Iadarola,b* Jan Stolkd

a

Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126, Milan, Italy.

b

Department of Biology and Biotechnologies "L. Spallanzani", Biochemistry Unit, University of

Pavia, Italy. c

Department of Molecular Medicine, Biochemistry Unit, University of Pavia, Italy.

d

Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands.

*Corresponding authors: Cristina Airoldi Department of Biotechnology and Biosciences, University of Milano-Bicocca, P.zza della Scienza 2, 20126, Milan, Italy. Tel. +39 02 6448 3303 Fax. +39 02 6448 3565 [email protected]

1 ACS Paragon Plus Environment


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Paolo Iadarola Department of Biology and Biotechnologies "L. Spallanzani", Biochemistry Unit, University of Pavia, Italy. Tel. +39 0382 98 7264 Fax. +39 0382 423108 [email protected]

KEYWORDS: α-1-antitrypsin COPD, Exhaled Breath Condensate, NMR-based metabolomics.

ABSTRACT The metabolomic analysis of Exhaled Breath Condensate (EBC) may provide insights on both the pathology of pulmonary disorders and the response to therapy. This pilot study describes the ability of Nuclear Magnetic Resonance (NMR)-based metabolomics to discriminate α1-antitrypsin deficient (AATD)-patients, who were diagnosed with moderate to severe emphysema, from healthy individuals. Comparative analysis of samples from these two homogeneous cohorts of individuals resulted in the generation of NMR profiles that were different from both a qualitative and a quantitative point-of-view. Among the identified metabolites that separated patients from controls, acetoin, propionate, acetate and propane-1,2 diol were those presenting the biggest difference. Unambiguous confirmation that the two groups could be completely differentiated on the basis of their metabolite content came from the application of univariate and multivariate statistical analysis (PCA, PLS-DA and OPLS-DA). MetaboAnalyst 3.0 platform, used to define a relationship among metabolites, allowed to observe that pyruvate metabolism is the most-involved pathway, most of metabolites being originated from pyruvate. These preliminary data suggest that NMR, with its ability to differentiate the metabolic fingerprint of EBC of AATD patients from that of healthy 2 ACS Paragon Plus Environment

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controls, has a potential “clinical applicability” in this area.

INTRODUCTION A way to access the lung epithelial lining fluid with minimal technical skills and without any discomfort or risk for both children and adults alike is sampling of exhaled breath condensate (EBC). Obtained by cooling exhaled air from spontaneous breathing in a commercially available equipment, this matrix is made of water vapor containing volatile and nonvolatile substances from the central airways, which are likely to reflect the composition of the airway-lining fluid.1,2 As such, it provides information complementary to that of BronchoAlveolar Lavage fluid (BALf) and induced sputum (IS), thus potentially offering several applications for diagnosis of lung diseases. As EBC collection and processing procedures have begun to improve, biases relative to methodological questions (which may represent a potential source of error in the determination of analyte concentration) have been greatly mitigated, although accurate protocols still need to be fully standardized and validated.3,4,5,6 Despite these limitations, EBC is presently being used as a reliable matrix for investigations on a variety of lung disorders, including asthma, bronchiectasis, adult respiratory distress syndrome, cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD).7,8,9,10, 11,12,13 The content of proteins in EBC (EBC proteomics) has also been investigated. The application of two-dimensional electrophoresis (2-DE) and/or liquid chromatography (LC) combined to mass spectrometry (MS) resulted in the identification of a good number of proteins, some of which hold the promise to be attractive tools for monitoring alterations in the respiratory tract.14,15,16,17,18 More recently, nuclear magnetic resonance (NMR) spectroscopy has shown to be a promising approach for the identification in EBC of small molecules of prognostic and predictive significance.19,20,21,22 This was the advent of metabolomics, a systematic study of the whole set of low-molecular weight molecules in a biological sample (cell, tissue, biofluid etc.) which are the end-products of cellular processes or responses to an environmental stress.23,24,25,26



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Being the understanding of disease processes at the metabolic level still poor, profiling the metabolome is a means to identify molecules that, for example, may discriminate patients from healthy controls. While capturing the entire metabolome would require a multiplatform approach, due to the huge diversity of chemical characteristics/abundance of analytes,27,28 a good snapshot of the system under investigation may be also provided by a single analytical method. This is the case of NMR spectroscopy. While not being very sensitive, NMR is characterized by inherent, distinctive advantages, which include minimal sample preparation, rapid acquisition time of spectra and the possibility to perform an untargeted analysis, also with respect to the chemical nature of metabolites. These peculiarities have soon promoted NMR-based metabolomics of EBC to the rank of a valuable method for an efficient investigation of a variety of lung diseases.19,20,29,30 Thus, based on these encouraging results, the question raised of whether NMR could be a helpful tool also for the study of α1-antitrypsin deficiency (AATD). Experiments described in this paper confirm the ability of this technique to identify metabolic patterns that discriminate the EBC metabolome of protease inhibitor genotype ZZ-α1-antitrypsin deficient (PiZZ-AATD) patients associated with pulmonary emphysema from healthy controls.

MATERIALS AND METHODS Materials Unless otherwise stated, all materials used in this work were from Sigma (St. Louis, MO, USA). Reagent-grade water used to prepare all solutions was obtained from a Milli-Q (Millipore, Bedford, MA, USA) purification system. Subjects 22 subjects participated in this study. All of them were enrolled by the Department of Pulmonology of Leiden University Medical Center, The Netherlands. The study protocol was approved by the Research Ethics Committee of this institute and each subject gave his informed consent before


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entering the study. Eleven (8 males and 3 females; 51 ± 8 y) were PiZZ-AATD patients with pulmonary emphysema recruited from the Dutch AIR database and eleven (4 males and 7 females; mean age; 54 ± 8 y), were non-smoking healthy volunteers, with normal spirometry results and no significant history of respiratory diseases. Some of these latter (controls) were the spouses of the AATD patients. All individuals were asked to abstain from food intake and drinking alcohol at least 12 hours before EBC collection. Anthropometrics and demographic data of subjects investigated are shown in Table 1 (see Results section). EBC collection Exhaled breath condensate was collected using an RTube condensing kit (Respiratory Research Inc. USA). Briefly, each subject was asked to breath (while wearing a nose clip) for ten minutes at tidal volume through a mouth piece into a polypropylene tube that was placed in an aluminum sleeve stored frozen at −40 °C overnight before collection was started.31 This procedure allowed vapors, aerosols and moisture in exhaled breath to condense along the walls of the tube. The volume of condensate collected was typically 1.0 ± 0.1 mL for controls and around 700 ± 50 µL for AATD patients. To preserve the metabolite concentration and block all possible metabolic activity, this solution was homogenized by gently shaking and divided in aliquots (around 300 µL each) which were stored immediately at −80 °C until analysis. At the time of analysis, samples were thawed and the correct analyte concentration determined by measuring sample conductivity. The conductivity values of specimens were used as a correction factor for dilution of respiratory droplets by the water of vaporization that determines the variability in the concentration of analytes. Measurements of conductivity were performed as previously reported.32 Although the design of the system prevented salivary contamination, all EBC samples collected were examined for amylase activity by using the alpha-amylase ESP 1491300 kit (detection limit 0.003 U/mL; Boehringer Mannheim, Germany). NMR spectroscopy Samples were added of 10% D2O before being submitted to NMR analysis. 4,4-dimethyl-4-



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silapentane-1-sulfonic acid (DSS, final concentration 0.1 mM) was also added to each sample as internal reference of both concentration and chemical shift. The pH of each sample was verified with a Microelectrode (Mettler-Toledo, Columbus, Ohio, USA) for 5 mm NMR tubes and, if necessary, adjusted to a value of 7.4 by the addition of small amounts of NaOD or DCl. Only slight pH changes (0.08-0.2 pH units) were necessary to have all samples analyzed under the same experimental conditions. The acquisition temperature was 298 K. All spectra were acquired on a Bruker AVANCE III 600 MHz NMR spectrometer equipped with a QCI (1H,

13

C,

15

N/31P and 2H

lock) cryogenic probe. 1D 1H-NMR spectra were recorded with water suppression (noesygppr1d pulse sequences in Bruker library) and 1024 scans, spectral width of 20 ppm, relaxation delay of 5 s. They were processed with a line broadening of 0.3 Hz and automatically phased and baseline corrected. Chemical shifts values were internally calibrated to the DSS peak at 0.0 ppm. Metabolite identification and assignment were performed with the support of 2D NMR experiments, the Human Metabolome Database,33,34,35 the Biological Magnetic Resonance Data Bank36 and the SMA analysis tool integrated in MestreNova software.37 In particular, 1H,1HTOCSY (Total Correlation SpectroscopY) spectra (dipsi2esgpphpp pulse sequence in Bruker library) were acquired with 40 scans and 512 increments, a mixing time of 80 ms and relaxation delay was 2 seconds.

1

H,13C-HSQC (Heteronuclear Single Quantum Coherence) spectra

(hsqcetgppr pulse sequence in Bruker library) were acquired with 48 scans and 256 increments, a relaxation delay of 2.5 s. For metabolite quantification, the GSD (global spectrum deconvolution) algorithm, available in the MNova software package of Mestrelab (MestReNova v 10.0, 2016 Mestrelab Research S.L.) was exploited.37 In this way, overlapping regions were deconvoluted and absolute quantification performed also for metabolites with resonances in rare crowded spectral areas (for example signals from acetone and acetoin, Figure S1 of Supporting Information). For each compound, the mean value of the different assigned signals was determined.


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Statistical analysis Before starting with the analysis of all samples, the reproducibility of runs was checked by assessing within-day and between-day repeatability, according to the protocol previously described and validated.38,39 Briefly, each sample was run three times in the course of the same day and on three consecutive days and NMR spectra compared. Statistical analysis was performed exploiting the real-time interactive web-based application Metaboanalyst 3.0.40,41,42 To discriminate different EBC samples through NMR spectra, we carried out both univariate and multivariate statistical data analysis. Data were uploaded as a table reporting metabolite concentrations for each sample, where a sample corresponds to the EBC spectrum of a different individual, classified as healthy (H) or diseased (D). Data were transformed with log normalization and scaled with Pareto scaling, increasing the contribution of lower concentration metabolites in the generated models compared with models where no scaling has been employed. Firstly, a univariate analysis, based on volcano plot, which is a combination of Fold Change (FC) analysis and t-tests, was performed to achieve a preliminary overview about features that are potentially signiﬁcant in discriminating controls from patients. Important features were selected by volcano plot with fold change threshold 2 and t-tests threshold 0.1. Then different multivariate analyses were applied to detect EBC metabolites trends and clusterings in both unsupervised (i.e., no prior group knowledge), through Principal Component Analysis (PCA), and supervised manner, by Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Finally, a hierarchical cluster analysis was performed and clustering was represented in the form of dendogram (distance measure euclidean, clustering algorithm Ward).



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RESULTS Previous proteomic studies performed on pooled EBC samples of healthy subjects and AATD patients resulted in the generation of a panel of differentially expressed proteins, which are supposed to originate directly from the airways and the gas exchange unit of the lungs.17 While being exploratory, this study laid the foundation for the future identification of a set of proteins that, by monitoring the disorder, would become the classifier for discriminating controls from patients. Based on these observations, the question arose whether exploring the EBC metabolite content would have resulted in the production of data complementary to the proteomic ones, correlating with the disease status of individuals. To verify this hypothesis, single EBC samples from 22 subjects (11 controls and 11 patients) were analyzed in a blind experiment in which the researchers who carried out the analyses and interpreted the results were blinded regarding the clinical conditions of individuals, i.e. who was in the control and in the experimental groups. The only information given to coworkers was that they were assigned EBCs from 22 individuals for NMR analysis. Demographic data of subjects considered in this study are reported in Table 1. To generate 1H-NMR profiles characterized by high reproducibility, all EBC samples have been analyzed consecutively the same day following the procedure detailed in the experimental section. To gain insights into the metabolic information contained in these NMR spectra, the different resonances were assigned to specific metabolites either by resorting to published data7 and by performing 2D-NMR experiments (1H,1H-TOCSY and 1H,13C-HSQC, Figure S2 of Supporting Information). The complete list of these metabolites and their corresponding resonance values are reported in Table 2. Resonance assignments are shown in Figure 1. Consistently with previous studies 1919,21,22 spectra were characterized by the signals of about 20 compounds, which included monocarboxylic acids, alcohols, ketones and aminoacids. The signal of a not-yet identified compound (molecule 18 in Table 2) that, on the basis of chemical shifts and


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integral values was tentatively assigned to the -OR group of an ester, was also observed. The correct number of nuclei generating each proton signal of this “unknown” compound was confirmed through the acquisition of 1H,13C-HSQC spectra in a multiplicity-edited mode (Figure S2B). Its resonances, together with those belonging to a variety of other metabolites (acetoin, benzoate, butyrate, isopropanol, propionic acid, propylene glycol) were of great interest in that they were present only in the NMR spectra of a limited number of samples. This allowed distinguishing a group (indicated as group-1) of subjects containing all or a significant number of the mentioned metabolites from another one in which they were completely absent (indicated as group-2). These qualitative differences are well evidenced in Figure 2, in which the NMR spectrum shown in panel A is representative of the samples characterized by higher abundance of resonances (group-1) and that shown in panel B is representative of EBCs with a poorer number of resonances (group-2). Based on these findings, we speculated that differences between the two groups could reflect different clinical conditions of individuals analyzed, in other words could well depict the metabolite content of EBC from controls and diseased subjects. Obviously, we were well aware that these findings could be interpreted either as the complete absence of such molecules in the EBC of group-2 subjects, or as their presence in a concentration below the detection limit of the approach (estimated to be around 0.05 µM). Whatever the way of interpreting, the presence/absence of these metabolites sounded as a good discriminant to assign the subjects to a well-defined group. Further support to the potential important role of these compounds came from the quantitative analysis of data. The concentration of all metabolites for each subject investigated are provided in Table S1 of Supporting Information. The accurate integration of signals, in fact, allowed to evidence that the levels of several metabolites in group-1 subjects was increased three to three hundred-fold compared to those of group-2, with p values ranging from 8.66E-01 to 5.74E-08 (Table 3). At variance, methanol concentration in group-1 samples resulted lower (Table S1 of Supporting Information).



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From the qualitative and quantitative information obtained from EBCs analyzed, the unifying view emerges that the population under investigation was made of two separate, homogeneous cohorts. Thus, at the test disclosure, when profiles were matched to individuals, not surprisingly each single subject whose EBC contained all/a part of additional metabolites (group-1) could be unambiguously assigned to the cohort of AATD patients (from now on indicated as D, diseased) and those whose EBC was less abundant in the number/amount of the above mentioned metabolites (group-2) were assigned to controls (indicated as H, healthy). To confirm and somehow “validate” our findings, data were submitted to a multivariate statistical analysis. Principal Component Analysis (PCA) was applied as a first-step exploratory unsupervised analysis to obtain a general overview of sample distribution. The PCA 2D score plot shown in Figure 3A unambiguously evidences that subjects investigated could be discriminated on the basis of their metabolite content, the two different groups (H and D) being completely separated. To corroborate data and reinforce classification, the Partial Least-Squares Discriminant Analysis (PLS-DA) was applied as a supervised method. In addition, the PLS-DA score plot shown in Figure 3B confirms the existence of two clearly distinct groups of individuals, whose EBC is characterized by different metabolite concentrations. To obtain a graphical representation of the extent to which each metabolite accounts for the variance in the data and show the relationships/correlations between the different metabolites, these data were also used to generate the corresponding loading plots. The individual EBC metabolites responsible for the variation of the first PCs (PC 1 and PC 2) obtained from PCA and PLS-DA analyses are shown in Figures S3 and S4 respectively of Supporting Information. Taken together, these findings, while confirming that NMR was a reliable platform for detection of polar compounds in human EBC, strengthened the hypothesis that the difference in the content of metabolites between controls and AATD patients could be correlated to the disease. The same data set was also used to generate an Orthogonal Partial Least Squares Discriminant


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Analysis (OPLS-DA) model whose score plot is reported in Figure 3C. The resulting supervised models were tested by iteratively predicting the class membership of every sample. Results allowed to evaluate the goodness of ﬁt (R2) and prediction (Q2), for which acceptable values must be ≥0.5.43 For the model describing H vs D we obtained R2 = 0.961 and Q2 = 0.928, indicating that the model (R2) is able to explain data variation, with a very good predictivity (Q2). Therefore, subjects with disease (D) can be clearly separated from healthy individuals (H). However, from the analysis of demographic data of the individuals considered in this study (Table 1) emerged that most of control subjects (H) were females (F), while, the majority of diseased individuals (D) were males (M). In light of this evidence, we thought that it was appropriate to verify that gender was not able to affect the results obtained from the statistical analysis. Therefore, PCA, PLS-DA and OPLS-DA analyses were repeated by grouping subjects according to gender. Results reported in Figure 4 clearly confirm that females and males cannot be efficiently distinguished on the bases of NMR data. In particular, components 1 (T score) and 2 (orthogonal T score) in the score plots of Figure 4C were unable to give rise to a satisfactory classification model, obtaining R2 = 0.313, with Q2 = -1.31. Since a robust model is expected to have R2, Q2 ≥ 0.5,43 we conclude that gender does not affect the interpretation and the statistical analysis of our samples. The final confirmation that patients and controls could be easily discriminated came from the dendogram (shown in Figure 5) generated by cluster analysis of the NMR profiles.

DISCUSSION Exhaled breath condensate contains both water-soluble metabolites (that are either derived from the lung itself or diffusing through the epithelium) and volatile organic compounds (VOCs) that are produced during most metabolic reactions. It is collected easily and non-invasively by any patient suffering for lung diseases to meet the need for new biomarker identification and to increase



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understanding of the disorder. A decisive evidence that single EBC samples can be analyzed by 1HNMR for its content in metabolites has been provided by several authors who showed how identification of these metabolites might serve to diagnose a variety of respiratory diseases.19,20,21,22,29,30 The results from these investigations, taken together, clearly demonstrated the ability of this technical approach to measure with high efficiency qualitative and quantitative spectral differences between healthy and diseased cohorts of individuals. In particular, the metabolic fingerprints obtained from the analysis of EBC of healthy people and subjects affected by smoking-related diseases21 or from asthmatic,22 and COPD patients,20 have confirmed the hypothesis that differences in the metabolite content could correlate with the disease status. The significant increase/decrease in the amount of few metabolites among those identified in these studies was shown by these authors to be responsible for intergroup separation. In light of these interesting results, the metabolite content of EBC collected from subjects with pulmonary emphysema associated with α1-antitrypsin deficiency was explored in this paper. In an effort to derive information for a disorder that is the underlying cause of approximately 1-3% of COPD cases,44 this study was focused on the analysis of EBCs from genetically proven severe AATD patients who display extremely low AAT levels ( 300-fold) of propionate observed in patients aroused particular interest. This


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finding was in concordance with results from other authors who noticed in COPD patients the same sharp increase of propionate with respect to healthy subjects.21 According to these authors, a possible anti-inflammatory action can be hypothesized for propionate, given the ability of shortchain fatty acids to regulate several leukocyte functions, including production of cytokines, eicosanoids, and chemokines.51 In an effort to explain this large variation between our two groups, we also speculated that the glucocorticoid receptor agonist (with anti-inflammatory effects) administered to them in the form of propionate salt could be responsible for the high amount of this metabolite in patients’ EBC. However, the experimental evidence that the potential contribution of this drug to the final concentration of propionate in EBC, if any, was only partial, allowed to embrace the thesis proposed by the above mentioned authors.21 In fact: i) not all patients were administered this drug; ii) not all treated-patients showed the same level of propionate in their EBC (one of them did not contain propionate at all) and iii) although patients have also been treated with a drug administered in the form of fumarate salt, no traces of fumarate have been detected (see Table S1 of Supporting Information). Given the role of propionate in the inhibition of cholesterol synthesis, its increase may also suggest the involvement of lipid metabolism. The increase of acetate (36-fold higher in patients) and butyrate (19-fold higher) are in line with this hypothesis. The amount of butyrate is lower because it is oxidized through the fatty acid β-oxidation and tricarboxylic acid cycle pathways in the mitochondria, forming two acetate molecules for each butyrate molecule.

LIMITATIONS OF THE STUDY It is probably fair to say that a limitation of the present study is the sample size of individuals investigated. We would like to note that, given the prevalence of the disease considered, patients were enrolled with difficulty and the group size was necessarily poor. However, based on the values of both FEV1 and oxygen uptake capacity (gas transfer) by the lungs, and on a well-recorded



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clinical history of each patient, this population appeared indeed particularly homogeneous. This feature, no matter how large the cohort was, made this set of samples uniform and, in our opinion, it could represent a strength of the work rather than a weakness. Of course, we are aware that a highquality set of samples does not necessarily eliminate the risk of relying on poor evidence of data and that, to obtain more concrete answers about a condition that is largely still shrouded in mystery, larger-scale studies will be essential. The fact that the control group contained mainly females and that patients were mainly males may be seen as another limitation in terms of reliability of data, but this was related to the spouse-control approach. Analysis of EBC from spouses might have reduced the effect of environmental factors. Experimental evidence has shown that this did not bias our results, no significant sex-related difference being emerged from the statistical analysis.

CONCLUSIONS In an effort to provide a novel context for facilitating interpretation of lung symptoms present in subjects with AATD, aim of this pilot work was the identification of metabolites that could be biomarkers of disease. This first attempt resulted in the identification of a good number of metabolites differentially expressed between healthy controls and patients thus providing just a taste of what is possible at the metabolomic scale on this disorder. It could be argued that, being common to other pulmonary disorders, these metabolites are not very specific to AATD. However, given the common clinical traits between AATD and other pulmonary disorders, this finding was not surprising. The experimental data reported here confirm that some relevant pathways shown to be involved in other lung disorders are most likely deregulated also in AATD. However, while the degree of agreement between our results and those previously published by other authors (on pulmonary disorders different from AATD) was even beyond our expectations in terms of altered metabolites identified, the oscillation pattern of changes for some of them was much higher in our


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patients. In our opinion, this could represent a peculiar trait for AATD patients. Nevertheless, the above-cited concordance of data is highly unlikely to be due to mere chance and may assess the analytical strength of NMR. In conclusion, our findings show that metabolomics analysis of EBC from subjects with AATD and healthy controls is possible and reproducible, resulting in a 100% correct classification rate of individuals by using a good number of compounds. In this context, these results may indeed represent a proof of principle for improving the knowledge of the disease. Aim of future studies will be analysing EBC from the same patients taken at baseline and at five consecutive visits (up to 60 weeks after baseline). This longitudinal study will provide us information about the trend of metabolite alterations over time, thus allowing correlating their concentration to the severity of the condition and, possibly, predicting progression or reflect changes induced by effective treatment of the disorder.

SUPPORTING INFORMATION Figure S1. 1H-NMR spectrum showing partially overlapped signals from acetoin and acetone. Figure S2. 1H,1H-TOCSY and edited 1H,13C-HSQC spectra of representative samples. Table S1. Metabolite concentrations (µM) in healthy control and patient EBC samples as determined by NMR. Figure S3. Loadings plot for the selected PCs. Figure S4. Loadings plot for the selected PCs. Table S2. Results of pathway enrichment analysis and pathway topology analysis.

ACKNOLEDGEMENTS



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This study was partly funded by the e-ALTA award 2013 offered to Dr. Marco Fumagalli by Grifols SA, Barcelona, Spain. Dr. Cristina Airoldi thanks Fondazione CARIPLO for funding project 2015-0763.

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TABLES Table 1. Demographic data of individuals considered in this study. Subjects Investigated* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Age (years)

Sex

FEV1 % **

FEV1 (L)***

61 55 43 57 42 56 46 64 62 64 58 61 59 46 51 51 61 41 57 50 54 52

F M F F F F M M F M F M M M F M M M M F M F

84 91 88 83 77 79 90 83 81 76 78 45 52 31 34 35 39 38 69 55 48 52

4.27 3.71 3.88 4.56 4.02 3.65 4.41 3.85 4.32 3.72 3.82 1.56 1.83 1.48 1.63 1.51 1.66 1.75 1.89 1.57 1.46 1.54

*Subjects numbered 1 to 11 belong to group “1” (healthy controls); from 12 to 22 to group “2” (diseased) ** Forced Expiratory Volume (in % predicted) *** Forced Expiratory Volume (in litres)


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Table 2. Metabolite assignments and chemical shifts of distinguishable peaks. Assigned number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Metabolite 2,3-butanediol Acetate Acetoin Acetone Alanine Benzoate Butyric acid Ethanol FA (fatty acid) Formate Glycerol Isopropanol Lactate Methanol Propionic acid Propylen glycol Pyruvate Unknown

Chemical shift (ppm) 3.71 (m) 3.61 (m) 1.13 (d) 1.92 (s) 4.42 (q) 2.21 (s) 1.37 (d) 2.22 (s) 3.77 (q) 1.47 (d) 7.87 (m) 7.54 (m) 7.47 (m) 2.16 (t) 1.56 (tq) 0.90 (t) 3.65 (q) 1.17 (t) 0.87-0.83 (m) 8.45 (s) 3.77 (ddd) 3.64 (dd) 3.55 (dd) 4.02 (hept) 1.17 (d) 4.10 (q) 1.32 (d) 3.34 (s) 1.04 (t) 2.16 (q) 3.87 (td) 3.54 (dd) 3.43 (ddd) 1.13 (d) 2.46 (s) 4.05 (t) 1.66 (p)

Table 3. Important features determined by volcano plot for metabolites identified in analyzed EBCs (with fold change threshold = 2 and t-tests threshold = 0.1). Compound Acetate

FC log2(FC) p value -log10(p) 36.564 5.1923 5.74E-12 11.241



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

2,3-butanediol Propionic acid Lactate Butyrate Acetone Benzoate FA (fatty acid) Formate Propylen glycol Alanine Ethanol Unknown Acetoin Isopropanol

19.766 337.73 3.5926 16.734 17.383 24.061 27.498 2.5303 69.242 30.5 3.9851 29.697 152.36 11.939

4.305 8.3997 1.845 4.0647 4.1196 4.5886 4.7813 1.3393 6.1136 4.9307 1.9946 4.8922 7.2514 3.5777

1.03E-08 1.99E-08 4.74E-07 1.69E-06 1.85E-06 3.10E-06 3.49E-05 3.65E-05 8.66E-05 0.00013 0.000179 0.002555 0.003773 0.014526

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7.9854 7.7009 6.3242 5.7726 5.7333 5.5082 4.457 4.4376 4.0623 3.8853 3.7462 2.5926 2.4234 1.8379

FIGURES CAPTIONS Figure 1. Representative 1H-NMR spectrum of an EBC sample recorded at 600 MHz, pH 7.4, 25 °C. The correspondence between peak numbering and metabolite assignments is shown in Table 2. Figure 2. Representative NMR profiles from individuals assigned to group-1 (Panel A) and group“2 (Panel B). Figure 3. A) PCA, B) PLS-DA and C) OPLS-DA score plots between controls (H) and patients (D). The explained variances are shown in brackets. Ellipses display 95% confidence regions. In red are diseased (D) subjects, in green the healthy controls (H). Figure 4. A) PCA, B) PLS-DA and C) OPLS-DA score plots between males and females. The explained variances are shown in brackets. Ellipses display 95% confidence regions. In red are the females (F), in green males (M). Figure 5. Clustering results shown as dendrogram (distance measure using euclidean, and clustering algorithm using ward.D).


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Figure 6. Matched pathways according to p values from pathway enrichment analysis and pathway impact values from pathway topology analysis.

FIGURES Figure 1.

δ/ppm



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Figure 2.

δ/ppm

Figure 3.


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Figure 4.

Figure 5.



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Figure 6.


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SCHEME CAPTION 31 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Scheme 1. Biochemical reactions by which pyruvate may originate all other C2 to C4 compounds detected in EBC of analyzed subjects.

SCHEME Scheme 1.

TOC


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1H NMR To Explore the Metabolome of Exhaled Breath Condensate

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