Early detection of bacterial pneumonia by characteristic induced odor signatures

Abstract

Introduction: The ability to detect pathogenic bacteria before the onsets of severe respiratory symptoms and to differentiate bacterial infection allows to improve patient-tailored treatment leading to a significant reduction in illness severity, comorbidity as well as antibiotic resistance. As such, this study refines the application of the non-invasive Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS) methodology for real-time and early detection of human respiratory bacterial pathogens in the respiratory tract of a mouse infection model.

Methods: A real-time analysis of changes in volatile metabolites excreted by mice undergoing a lung infection by Staphylococcus aureus or Streptococcus pneumoniae were evaluated using a SESI-HRMS instrument. The infection status was confirmed using classical CFU enumeration and tissue histology. The detected VOCs were analyzed using a pre- and post-processing algorithm along with ANOVA and RASCA statistical evaluation methods.

Results: Characteristic changes in the VOCs emitted from the mice were detected as early as 4-6 h post-inoculation. Additionally, by using each mouse as its own baseline, we mimicked the inherent variation within biological organism and reported significant variations in 25 volatile organic compounds (VOCs) during the course of a lung bacterial infection.

Conclusion: the non-invasive SESI-HRMS enables real-time detection of infection specific VOCs. However, further refinement of this technology is necessary to improve clinical patient management, treatment, and facilitate decisions regarding antibiotic use due to early infection detection.

Keywords: Exhalomic analysis; Lung infection VOC; SESI-HRMS; Staphylococcus aureus; Streptococcus pneumoniae.

Fig. 1

Presence of S. aureus and S. pneumoniae causing lung infection in mice as confirmed by histology and CFU determination. (a) Representative example of lung tissue from mice intratracheally inoculated with different strains of S. aureus or S. pneumoniae using Gram staining. Black arrows indicate bacteria. The scale bars in images equal 20 μm. (b) Quantification of recovered CFUs after processing of lavage and tissue from the lung of infected mice

Fig. 2

RASCA analysis of intratracheally inoculated mice reveals characteristic bacterial-induced metabolic trajectories during the course of infection. (a) Explained variance of the effects included in the experimental design as evaluated with RASCA. A large part of the variance is attributed to the residuals (Res) reflecting the between-mice variability followed by the strain (S) and time (T) main effects, which were both significant as indicated by the permutation test, whereas the strain – time interaction (SxT) was not significant. (b) The average scores on the second and third principal components of the time effect are shown. PC2 shows a decreasing trend after the inoculation step whereas PC3 shows an increasing trend

Fig. 3

Distinct metabolic trajectories in infected mice. (a) Example time traces of five metabolic features contributing most to the increasing time trend in infected mice pre vs. post infection time (dashed line). (b) Example time traces of five metabolic features contributing most to the decreasing time trend in infected mice pre vs. post infection time (dashed line). Mean time traces are presented for mice infection (orange line) model along with 95% confidence intervals (light orange areas)