Details

Autor(en) / Beteiligte

• Purcaro, Giorgia
• Nasir, Mavra
• Franchina, Flavio A.
• Rees, Christiaan A.
• Aliyeva, Minara
• Daphtary, Nirav
• Wargo, Matthew J.
• Lundblad, Lennart K. A.
• Hill, Jane E.

Titel

Breath metabolome of mice infected with Pseudomonas aeruginosa

Ist Teil von

• Metabolomics, 2019-01, Vol.15 (1), p.10-10, Article 10

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2019

Quelle

MEDLINE

Beschreibungen/Notizen

• Introduction The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa . Objectives To determine a discriminatory core of molecules in the “breath-print” of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific “breath-print” signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20–25, 2014 . https://doi.org/10.1111/2049-632X.12107 ). Methods Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model. Results Three hundred and one molecules were significantly different between animals infected with P. aeruginosa , and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa . Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa . A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups. Conclusions To the best of our knowledge, this is the first study to report on a ‘core’ murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.