Detection of Staphylococcus aureus in cystic fibrosis patients using breath VOC profiles.

Staphylococcus aureus (S. aureus) is a common bacterium infecting children with cystic fibrosis (CF). Since current detection methods are difficult to perform in children, there is need for an alternative. This proof of concept study investigates whether breath profiles can discriminate between S. aureus infected and non-infected CF patients based on volatile organic compounds (VOCs). We collected exhaled breath of CF patients with and without S. aureus airways infections in which VOCs were identified using gas chromatography-mass spectrometry. We classified these VOC profiles with sparse partial least squares discriminant analysis. Multivariate breath VOC profiles discriminated infected from non-infected CF patients with high sensitivity (100%) and specificity (80%). We identified the nine compounds most important for this discrimination. We successfully detected S. aureus infection in CF patients, using breath VOC profiles. Nine highlighted compounds can be used as a focus point in further biomarker identification research. The results show considerable potential for non-invasive diagnosis of airway infections.

Identification of Pseudomonas aeruginosa and Aspergillus fumigatus mono- and co-cultures based on volatile biomarker combinations.

Volatile organic compound (VOC) analysis in exhaled breath is proposed as a non-invasive method to detect respiratory infections in cystic fibrosis patients. Since polymicrobial infections are common, we assessed whether we could distinguish Pseudomonas aeruginosa and Aspergillus fumigatus mono- and co-cultures using the VOC emissions. We took headspace samples of P. aeruginosa, A. fumigatus and co-cultures at 16, 24 and 48 h after inoculation, in which VOCs were identified by thermal desorption combined with gas chromatography - mass spectrometry. Using multivariate analysis by Partial Least Squares Discriminant Analysis we found distinct VOC biomarker combinations for mono- and co-cultures at each sampling time point, showing that there is an interaction between the two pathogens, with P. aeruginosa dominating the co-culture at 48 h. Furthermore, time-independent VOC biomarker combinations were also obtained to predict correct identification of P. aeruginosa and A. fumigatus in mono-culture and in co-culture. This study shows that the VOC combinations in P. aeruginosa and A. fumigatus co-microbial environment are different from those released by these pathogens in mono-culture. Using advanced data analysis techniques such as PLS-DA, time-independent pathogen specific biomarker combinations can be generated that may help to detect mixed respiratory infections in exhaled breath of cystic fibrosis patients.