Zhu Jiangjiang, Jiménez-Díaz Jaime, Bean Heather D, Daphtary Nirav A, Aliyeva Minara I, Lundblad Lennart K A, Hill Jane E
School of Engineering, University of Vermont, Burlington, VT 05405, USA.
J Breath Res. 2013 Sep;7(3):037106. doi: 10.1088/1752-7155/7/3/037106. Epub 2013 Jul 18.
Before breath-based diagnostics for lung infections can be implemented in the clinic, it is necessary to understand how the breath volatiles change during the course of infection, and ideally, to identify a core set of breath markers that can be used to diagnose the pathogen at any point during the infection. In the study presented here, we use secondary electrospray ionization-mass spectrometry (SESI-MS) to characterize the breathprint of Pseudomonas aeruginosa and Staphylococcus aureus lung infections in a murine model over a period of 120 h, with a total of 86 mice in the study. Using partial least squares-discriminant analysis (PLS-DA) to evaluate the time-course data, we were able to show that SESI-MS breathprinting can be used to robustly classify acute P. aeruginosa and S. aureus mouse lung infections at any time during the 120 h infection/clearance process. The variable importance plot from PLS indicates that multiple peaks from the SESI-MS breathprints are required for discriminating the bacterial infections. Therefore, by utilizing the entire breathprint rather than single biomarkers, infectious agents can be diagnosed by SESI-MS independent of when during the infection breath is tested.
在基于呼吸的肺部感染诊断方法能够应用于临床之前,有必要了解感染过程中呼吸挥发性物质是如何变化的,理想情况下,还需要确定一组核心的呼吸标志物,用于在感染的任何阶段诊断病原体。在本文介绍的研究中,我们使用二次电喷雾电离质谱(SESI-MS)对小鼠模型中铜绿假单胞菌和金黄色葡萄球菌肺部感染120小时期间的呼吸特征进行了表征,该研究共涉及86只小鼠。通过使用偏最小二乘判别分析(PLS-DA)来评估时间进程数据,我们能够证明,在120小时的感染/清除过程中的任何时间,SESI-MS呼吸特征分析都可用于对急性铜绿假单胞菌和金黄色葡萄球菌小鼠肺部感染进行可靠分类。PLS的变量重要性图表明,区分细菌感染需要SESI-MS呼吸特征中的多个峰。因此,通过利用整个呼吸特征而非单一生物标志物,SESI-MS能够在不考虑感染期间何时进行呼吸测试的情况下诊断感染病原体。