Metabolomic profiling of exhaled breath condensate for the diagnosis of pulmonary aspergillosis
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This study aims to ascertain the unique metabolic profile of exhaled breath condensate (EBC) samples in pulmonary aspergillosis (PA) patients, and explore their usefulness for the diagnosis of PA.A total of 133 patients were included in the study, including 66 PA patients (invasive pulmonary aspergillosis, n=3; chronic pulmonary aspergillosis, n=60; allergic bronchopulmonary aspergillosis, n=3) and controls (n=67). Ultra high-performance liquid chromatography coupled with high-resolution mass spectrometry(UHPLC-HRMS) was used to analyze EBC samples. Metabolic profiling of EBC samples that were collected from 22 CPA patients at various times during treatment (before treatment, <1 month, 1-2 months, 2-3 months, 3-6 months, and ≥6 months after treatment initiation) were performed using UHPLC-HRMS. Potential biomarkers were evaluated using cluster analysis, Venn diagram and receiver operating characteristic analysis (ROC).A total of 47 metabolites of potential interest were detected in the EBC samples. Further investigation showed that Asperpyrone C, Kotanin, Terphenyllin, Terrelumamide B, and Cyclotryprostatin D could be used as a diagnostic biomarker for PA. The classification between metabolic profiling of EBC samples from PA patients and controls was good with a sensitivity of 100%, specificity 89.6% for patients with PA, respectively. Venn diagram analysis of these biomarker candidates displayed three main types of compounds, which could be used for the further discrimination of aspergilloma and chronic cavitary PA. In addition, antifungal treatment had a limited influence on the value of the EBC results.This metabolomic approach using UHPLC-HRMS could be used as a noninvasive method for the diagnosis of PA.Keywords:
Exhaled breath condensate
Breath gas analysis
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Exhaled breath condensate
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Chronic obstructive pulmonary disease (COPD) is the third cause of death worldwide, presenting poor long-term outcomes and chronic disability. COPD is a condition with a wide spectrum of clinical presentations because its pathophysiological determinants relate to tobacco smoke, genetic factors, alteration of several metabolic pathways, and oxidative stress. Consequently, patients present different phenotypes even with comparable degrees of airflow limitation. Because of the increasing social and economic costs of COPD, a growing attention is currently paid to "omics" techniques for more personalized treatments and patient-tailored rehabilitation programs. In this regard, the systematic investigation of the metabolome (i.e., the whole set of endogenous molecules) in biomatrices, namely metabolomics, has become indispensable for phenotyping respiratory diseases. The metabolomic profiling of biological samples contains the small molecules produced during biological processes and their identification and quantification help in the diagnosis, comprehension of disease outcome and treatment response. Exhaled breath condensate (EBC), plasma and serum are biofluids readily available, with negligible invasiveness, and, therefore, suitable for metabolomics investigations. In this paper, we describe the latest advances on metabolomic profiling of EBC, plasma and serum in COPD patients.
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Breathomics, the multidimensional molecular analysis of exhaled breath, includes analysis of exhaled breath with gas-chromatography/mass spectrometry (GC/MS) and electronic noses (e-noses), and metabolomics of exhaled breath condensate (EBC), a non-invasive technique which provides information on the composition of airway lining fluid, generally by high-resolution nuclear magnetic resonance (NMR) spectroscopy or MS methods. Metabolomics is the identification and quantification of small molecular weight metabolites in a biofluid. Specific profiles of volatile compounds in exhaled breath and metabolites in EBC (breathprints) are potentially useful surrogate markers of inflammatory respiratory diseases. Electronic noses (e-noses) are artificial sensor systems, usually consisting of chemical cross-reactive sensor arrays for characterization of patterns of breath volatile compounds, and algorithms for breathprints classification. E-noses are handheld, portable, and provide real-time data. E-nose breathprints can reflect respiratory inflammation. E-noses and NMR-based metabolomics of EBC can distinguish patients with respiratory diseases such as asthma, COPD, and lung cancer, or diseases with a clinically relevant respiratory component including cystic fibrosis and primary ciliary dyskinesia, and healthy individuals. Breathomics has also been reported to identify patients affected by different types of respiratory diseases. Patterns of breath volatile compounds detected by e-nose and EBC metabolic profiles have been associated with asthma phenotypes. In combination with other -omics platforms, breathomics might provide a molecular approach to respiratory disease phenotyping and a molecular basis to tailored pharmacotherapeutic strategies. Breathomics might also contribute to identify new surrogate markers of respiratory inflammation, thus, facilitating drug discovery. Validation in newly recruited, prospective independent cohorts is essential for development of e-nose and EBC NMRbased metabolomics techniques. Keywords: Electronic nose, Chemical sensors, Volatile organic compounds, Biomarkers, Pattern recognition, Exhaled breath condensate, Metabolomics, NMR spectroscopy, Breathomics, Respiratory medicine, Asthma, Chronic obstructive pulmonary disease, Lung cancer.
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Abstract Exhaled breath condensate (EBC) is a biological fluid that contains trace amounts of secreted pulmonary proteins, and is emerging as a potentially valuable and non‐invasively obtained source of disease biomarkers. Proteome analysis of these samples could lead to the identification of prognostic indicators of airway diseases. The objective of this study was to develop a protocol for proteome analysis of EBC samples. In this report, an improved procedure for EBC sample preparation and concentration is presented, together with a method for comparison of the protein profiles between two groups. The presented approach enabled to study the condensed exhaled breath proteome for biomarker analysis, and revealed proteins not previously identified in an EBC proteomics approach. In a comparative pilot study, EBC protein profiles obtained from smokers and non‐smokers showed distinct differences and are illustrative for its potential in clinical studies. EBC from smokers contained higher concentrations of the more abundant proteins, such as cytokeratins, compared to non‐smokers, and calgranulin B was identified uniquely in EBC samples from smokers.
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Chronic obstructive pulmonary disease (COPD) is an increasing cause of global morbidity and mortality, with poor long-term outcomes and chronic disability. COPD is a condition with a wide spectrum of clinical presentations, with different phenotypes being identified even among patients with comparable degrees of airflow limitation. Considering the burden of COPD in terms of social and economic costs, in recent years growing attention has been given to the need for more personalized approaches and patienttailored rehabilitation programs. In this regard, the systematic analysis of metabolites in biological matrices, namely metabolomics, may become an essential tool in phenotyping diseases. Through the identification and quantification of the small molecules produced during biological processes, metabolomic profiling of biological samples has thus been proposed as an opportunity to identify novel biomarkers of disease outcome and treatment response. Exhaled breath condensate (EBC) and plasma/serum are fluid pools, which can be easily extracted and analyzed. In this review, we discuss the potential clinical applications of the metabolomic profiling of EBC and plasma/serum in COPD.
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Introduction: Metabolomics is the identification and quantification of small molecular weight metabolites in a biofluid. Specific profiles of volatile compounds and metabolites in EBC are potentially useful surrogate markers of distinct phenotypes in asthma. Aim: To detect possible differences in the metabolic profile between mild-to-moderate and severe asthmatic patients based on Gas Chromatography-Mass Spectrometry (GC-MS) metabolomics of EBC. Methods: 37 asthmatic patients at stable state for at least 6 months were studied (15 with severe asthma, non-smokers). EBC was collected with Ecoscreen device (Jaeger, Germany) and EBC metabolomics were analyzed with GC-MS. PCA and further PLS/OPLS analysis were performed to separate the two groups of asthmatic patients. T-test and VIP total were performed to detect the peaks that were statistically different in the groups. Results: PCA and PLS-DA UV Scaling could not separate mild-to-moderate from severe asthmatics whereas OPLS-DA UV Scaling demonstrated distinct separation between the two groups. In VIP total diagram 20 unidentified peaks differed significantly between the groups according to 2-tail t-test assuming unequal variances. Further analysis was performed to identify the metabolites and 2 metabolites have been identified so far using available libraries. Further identification will be performed using public available libraries and software systems. Conclusion: EBC is a difficult-to-handle biofluid for metabolomic analysis. GC-MS based metabolomics analysis of biofluids is a novel, promising method to distinguish different asthma phenotypes and could add information used in personalized therapy.
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Abstract Early diagnosis and treatment are critical for improving the survival of patients with lung cancer, which is the leading cause of cancer-related deaths worldwide. In this study, we investigated whether the metabolomics analysis of exhaled breath condensate (EBC) from patients with lung cancer can provide biomarkers that can be used for noninvasive screening for lung cancer diagnosis. EBC samples obtained from patients with lung cancer ( n = 20) and healthy individuals ( n = 5) were subjected to high-resolution metabolomics (HRM) using liquid chromatography–mass spectrometry (LC–MS). Univariate analysis, with a false discovery rate (FDR), q = 0.05, and hierarchical clustering analysis were performed to discover significantly different metabolites between the healthy controls and patients with lung cancer. This was followed by the identification of the metabolites using the METLIN database. Pathway analysis based on the identified metabolites revealed that arachidonic acid (AA) metabolism was the most significantly affected pathway. Finally, 5-hydroxyicosatetraenoic acid (HETE) ( m/z 343.2233, [M + Na] + ), a metabolite involved in AA metabolism, was found to be significantly higher in patients with lung cancer than in healthy counterparts. Our finding suggested that the HRM of EBC samples is a useful approach for identifying biomarkers for noninvasive screening for lung cancer diagnosis.
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To the Editors:
The metabolomic analysis of exhaled breath condensate (EBC) is a simple noninvasive approach for the study of respiratory system diseases. Previous studies introduced nuclear magnetic resonance (NMR)-based metabolomics as a method allowing a definite separation between healthy patients and patients with airway disease 1, 2. In these studies, the influence of external contaminants was also considered. de Laurentiis et al. 1 reported that the removal of interfering residual external contaminants was crucial for correct EBC analysis and they proposed a cleaning protocol for the “complete removal of the disinfectant signals” from the reusable parts of the condensers.
In order to verify the influence of the disinfectant signals we have compared the EBC 1H-NMR spectra of a healthy subject obtained after the standard cleaning protocol (disinfected for 15 min using a 1.5% Descogen™ solution and flushed for 15 min with water) recommended by the manufacturer’s guidelines and the International Consensus on EBC (fig. 1a) 3; one that was obtained after the cleaning protocol proposed by de Laurentiis et al. 1 (fig. 1b); and one obtained using a device without reusable condenser parts (fig. 1c). EBCs have been collected using an Anacon condenser (Biostec, Valencia, Spain). Sample collection and concentration (>99% water) were similar to those obtained in the procedure described by de Laurentiis …
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