Identification of microorganisms based on headspace analysis of volatile organic compounds by gas chromatography-mass spectrometry.

The identification of specific volatile organic compounds (VOCs) produced by microorganisms may assist in developing a fast and accurate methodology for the determination of pulmonary bacterial infections in exhaled air. As a first step, pulmonary bacteria were cultured and their headspace analyzed for the total amount of excreted VOCs to select those compounds which are exclusively associated with specific microorganisms. Development of a rapid, noninvasive methodology for identification of bacterial species may improve diagnostics and antibiotic therapy, ultimately leading to controlling the antibiotic resistance problem. Two hundred bacterial headspace samples from four different microorganisms (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumoniae) were analyzed by gas chromatography-mass spectrometry to detect a wide array of VOCs. Statistical analysis of these volatiles enabled the characterization of specific VOC profiles indicative for each microorganism. Differences in VOC abundance between the bacterial types were determined using ANalysis of VAriance-principal component analysis (ANOVA-PCA). These differences were visualized with PCA. Cross validation was applied to validate the results. We identified a large number of different compounds in the various headspaces, thus demonstrating a highly significant difference in VOC occurrence of bacterial cultures compared to the medium and between the cultures themselves. Additionally, a separation between a methicillin-resistant and a methicillin-sensitive isolate of S. aureus could be made due to significant differences between compounds. ANOVA-PCA analysis showed that 25 VOCs were differently profiled across the various microorganisms, whereas a PCA score plot enabled the visualization of these clear differences between the bacterial types. We demonstrated that identification of the studied microorganisms, including an antibiotic susceptible and resistant S. aureus substrain, is possible based on a selected number of compounds measured in the headspace of these cultures. These in vitro results may translate into a breath analysis approach that has the potential to be used as a diagnostic tool in medical microbiology.

Breath analysis as a potential diagnostic tool for tuberculosis.

SETTING: Cape Town, South Africa. OBJECTIVES: We investigated the potential of breath analysis by gas chromatography-mass spectrometry (GC-MS) to discriminate between samples collected prospectively from patients with suspected tuberculosis (TB). DESIGN: Samples were obtained in a TB-endemic setting in South Africa, where 28% of culture-proven TB patients had Ziehl-Neelsen (ZN) negative sputum smear. A training set of breath samples from 50 sputum culture-proven TB patients and 50 culture-negative non-TB patients was analysed using GC-MS. We used support vector machine analysis for classification of the patient samples into TB and non-TB. RESULTS: A classification model with seven compounds had a sensitivity of 72%, a specificity of 86% and an accuracy of 79% compared with culture. The classification model was validated with breath samples from a different set of 21 TB and 50 non-TB patients from the same area, giving a sensitivity of 62%, a specificity of 84% and an accuracy of 77%. CONCLUSION: This study shows that GC-MS breath analysis is able to differentiate between TB and non-TB breath samples even among patients with a negative ZN sputum smear but a positive culture for Mycobacterium tuberculosis. We conclude that breath analysis by GC-MS merits further research.

A profile of volatile organic compounds in breath discriminates COPD patients from controls.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an inflammatory condition characterized by oxidative stress and the formation of volatile organic compounds (VOCs) secreted via the lungs. We recently developed a methodological approach able to identify profiles of VOCs in breath unique for patient groups. Here we applied this recently developed methodology regarding diagnosis of COPD patients. METHODS: Fifty COPD patients and 29 controls provided their breath and VOCs were analyzed by gas chromatography-mass spectrometry to identify relevant VOCs. An additional 16 COPD patients and 16 controls were sampled in order to validate the model, and 15 steroid naïve COPD patients were sampled to determine whether steroid use affects performance. FINDINGS: 1179 different VOCs were detected, of which 13 were sufficient to correctly classify all 79 subjects. Six of these 13 VOCs classified 92% of the subjects correctly (sensitivity: 98%, specificity: 88%) and correctly classified 29 of 32 subjects (sensitivity: 100%, specificity: 81%) from the independent validation population. Fourteen out of 15 steroid naïve COPD patients were correctly classified thus excluding treatment influences. INTERPRETATION: This is the first study distinguishing COPD subjects from controls solely based on the presence of VOCs in breath. Analysis of VOCs might be highly relevant for diagnosis of COPD.

Volatile organic compounds in exhaled breath as a diagnostic tool for asthma in children.

BACKGROUND: The correct diagnosis of asthma in young children is often hard to achieve, resulting in undertreatment of asthmatic children and overtreatment in transient wheezers. OBJECTIVES: To develop a new diagnostic tool that better discriminates between asthma and transient wheezing and that leads to a more accurate diagnosis and hence less undertreatment and overtreatment. A first stage in the development of such a tool is the ability to discriminate between asthmatic children and healthy controls. The integrative analysis of large numbers of volatile organic compounds (VOC) in exhaled breath has the potential to discriminate between various inflammatory conditions of the respiratory tract. METHODS: Breath samples were obtained and analysed for VOC by gas chromatography-mass spectrometry from asthmatic children (n=63) and healthy controls (n=57). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate diseased from healthy children. A set of samples from both asthmatic and healthy children was selected to construct a model that was subsequently used to predict the asthma or the healthy status of a test group. In this way, the predictive value of the model could be tested. MEASUREMENTS AND MAIN RESULTS: The discriminant analyses demonstrated that asthma and healthy groups are distinct from one another. A total of eight components discriminated between asthmatic and healthy children with a 92% correct classification, achieving a sensitivity of 89% and a specificity of 95%. Conclusion The results show that a limited number of VOC in exhaled air can well be used to distinguish children with asthma from healthy children.

Development of accurate classification method based on the analysis of volatile organic compounds from human exhaled air.

Analysis of exhaled air leads to the development of fast accurate and non-invasive diagnostics. A comprehensive analysis of the entire range of volatile organic compounds (VOCs) in exhaled air samples will enable the identification of VOCs unique for certain patient groups. This study demonstrates proof of principle of our developed method tested on a smoking/non-smoking study population. Thermal desorption and gas chromatography coupled to time-of-flight mass spectrometry were used to analyse exhaled air samples. The VOC profiles obtained from each individual were combined into one final database based on similarity of mass spectra and retention indexes (RI), which offers the possibility for a reliable selection of compounds of interest. As proof of principle we correctly classified all subjects from population of smoking (N=11) and non-smoking (N=11) based on the VOC profiles available in their exhaled air. Support vector machine (SVM) analysis identified 4 VOCs as biomarkers of recent exposure to cigarette smoke: 2,5-dimethyl hexane, dodecane, 2,5-dimethylfuran and 2-methylfuran. This approach contributes to future development of fast, accurate and non-invasive diagnostics of inflammatory diseases including pulmonary diseases.