Bean Heather D, Zhu Jiangjiang, Sengle Jackson C, Hill Jane E
Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, USA.
Northwest Metabolomics Research Center, University of Washington School of Medicine, 850 Republican Street, Room S140, Seattle, WA 98109, USA.
J Breath Res. 2014 Oct 13;8(4):041001-41001. doi: 10.1088/1752-7155/8/4/041001.
Invasive methicillin-resistant Staphylococcus aureus (MRSA) infections are a serious health threat, causing an estimated 11,000 deaths per year in the United States. MRSA pneumonias account for 16% of invasive infections, and can be difficult to detect as the current state-of-the-art diagnostics require that bacterial DNA is recovered from the infection site. Because 60% of patients with invasive infections die within 7 d of culturing positive for MRSA, earlier detection of the pathogen may significantly reduce mortality. We aim to develop breath-based diagnostics that can detect Staphylococcal lung infections rapidly and non-invasively, and discriminate MRSA and methicillin-sensitive S. aureus (MSSA), in situ. Using a murine lung infection model, we have demonstrated that secondary electrospray ionization-mass spectrometry (SESI-MS) breathprinting can be used to robustly identify isogenic strains of MRSA and MSSA in the lung 24 h after bacterial inoculation. Principal components analysis (PCA) separates MRSA and MSSA breathprints using only the first component (p < 0.001). The predominant separation in the PCA is driven by shared peaks, low-abundance peaks, and rare peaks, supporting the use of biomarker panels to enhance the sensitivity and specificity of breath-based diagnostics.
耐甲氧西林金黄色葡萄球菌（MRSA）侵袭性感染对健康构成严重威胁，在美国估计每年导致11,000人死亡。MRSA肺炎占侵袭性感染的16%，由于目前最先进的诊断方法要求从感染部位获取细菌DNA，因此可能难以检测。由于60%的侵袭性感染患者在MRSA培养呈阳性后的7天内死亡，早期检测病原体可能会显著降低死亡率。我们旨在开发基于呼吸的诊断方法，能够快速、无创地检测葡萄球菌肺部感染，并在原位区分MRSA和甲氧西林敏感金黄色葡萄球菌（MSSA）。使用小鼠肺部感染模型，我们已经证明，二次电喷雾电离质谱（SESI-MS）呼吸指纹图谱可用于在细菌接种后24小时在肺部可靠地识别MRSA和MSSA的同基因菌株。主成分分析（PCA）仅使用第一个成分就能分离MRSA和MSSA的呼吸指纹图谱（p < 0.001）。PCA中的主要分离是由共享峰、低丰度峰和稀有峰驱动的，这支持使用生物标志物组来提高基于呼吸的诊断方法的灵敏度和特异性。
