Giorgia Purcaro1,2, Mavra Nasir3, Flavio A Franchina1,4, Christiaan A Rees3, Minara Aliyeva5, Nirav Daphtary5, Matthew J Wargo5, Lennart K A Lundblad6,7, Jane E Hill8,9. 1. Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH, 03755, USA. 2. Gembloux Agro-Bio Tech, University of Liège, Gembloux, 5030, Belgium. 3. Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Road, Hanover, NH, 03755, USA. 4. Department of Chemistry, University of Liège, Liège (Sart-Tilman), 4000, Belgium. 5. Larner College of Medicine, University of Vermont, 149 Beaumont Avenue, Burlington, VT, 05405, USA. 6. THORASYS Thoracic Medical Equipment Inc., 6560 de l'Esplanade, Suite 103, Montreal, QC, H2V 4L5, Canada. 7. Meakins-Christie Laboratories, McGill University, 1001 Boulevard Décarie, Montréal, QC, H4A 3J1, Canada. 8. Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH, 03755, USA. Jane.E.Hill@dartmouth.edu. 9. Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Road, Hanover, NH, 03755, USA. Jane.E.Hill@dartmouth.edu.
Abstract
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.
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.
Entities:
Keywords:
Breath; Comprehensive gas chromatography-time-of-flight mass spectrometer (GC×GC ToF MS); Pseudomonas aeruginosa; Volatile organic compounds (VOCs)
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