Giorgia Purcaro, Mavra Nasir, Flavio A Franchina, Christiaan A Rees, Minara Aliyeva, Nirav Daphtary, Matthew J Wargo, Lennart K A Lundblad, Jane E Hill
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Pathog Dis 71(1), 20-25, 2014. https://doi.org/10.1111/2049-632X.12107 ).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"10"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-018-1461-6","citationCount":"18","resultStr":"{\"title\":\"Breath metabolome of mice infected with Pseudomonas aeruginosa.\",\"authors\":\"Giorgia Purcaro, Mavra Nasir, Flavio A Franchina, Christiaan A Rees, Minara Aliyeva, Nirav Daphtary, Matthew J Wargo, Lennart K A Lundblad, Jane E Hill\",\"doi\":\"10.1007/s11306-018-1461-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>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.</p><p><strong>Objectives: </strong>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 ).</p><p><strong>Methods: </strong>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. 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引用次数: 18
摘要
呼吸中特定挥发性有机化合物的测量已被提出作为多种疾病的潜在诊断方法。在呼吸领域研究最多的细菌性肺部感染是由铜绿假单胞菌引起的。目的:确定四株铜绿假单胞菌(P. aeruginosa, PAO1, PA14, PAK, PA7)肺部感染小鼠“呼吸指纹”的鉴别核心分子。此外,我们试图推断菌株特异性的“呼吸指纹”特征,以研究概括遗传系统发育组的可能性(Stewart等人)。中华病理学杂志,2014(1):20-25。https://doi.org/10.1111/2049 - 632 x.12107)。方法:将呼气收集到Tedlar袋中,并在短时间内吸入热解吸管。后者被分析成一个全面的多维气相色谱联用飞行时间质谱仪。采用随机森林算法选择最具判别性的特征,建立预测模型。结果:感染铜绿假单胞菌的动物与假感染(PBS)或接种紫外光杀灭铜绿假单胞菌的动物之间有301个分子有显著差异。其中,九种代谢物可以用来区分三组,准确率为81%。分层聚类表明,呼吸的特征是由于对活细菌的特定反应,而不是一般的感染反应。此外,我们还鉴定了另外10种挥发性代谢物,这些代谢物可以区分感染不同铜绿假单胞菌菌株的小鼠。10种代谢物的系统图谱显示,PAO1和PA7是最明显的组,而PAK和PA14则散布在前两组之间。结论:据我们所知,这是第一个报告“核心”小鼠呼吸指纹的研究,以及呼吸中化合物之间的张力水平差异。我们提供鉴定(通过运行市售的分析标准),以预测铜绿假单胞菌感染的五种呼吸化合物。
Breath metabolome of mice infected with Pseudomonas aeruginosa.
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.