Identifying methicillin-resistant Staphylococcus aureus (MRSA) lung infections in mice via breath analysis using secondary electrospray ionization-mass spectrometry (SESI-MS).

Heather D Bean, Jiangjiang Zhu, Jackson C Sengle, Jane E Hill
Author Information
  1. Heather D Bean: Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, USA.
  2. Jiangjiang Zhu: Northwest Metabolomics Research Center, University of Washington School of Medicine, 850 Republican Street, Room S140, Seattle, WA 98109, USA.
  3. Jackson C Sengle: Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, USA.
  4. Jane E Hill: Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, USA.

Abstract

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.

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Grants

  1. P20 GM103496/NIGMS NIH HHS
  2. P20 RR021905/NCRR NIH HHS
  3. 8 P20 GM103496-07/NIGMS NIH HHS
  4. 5P20RR021905-07/NCRR NIH HHS

MeSH Term

Animals
Breath Tests
Humans
Male
Methicillin-Resistant Staphylococcus aureus
Mice
Mice, Inbred C57BL
Principal Component Analysis
Respiratory Tract Infections
Sensitivity and Specificity
Spectrometry, Mass, Electrospray Ionization
Staphylococcal Infections

Word Cloud

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