Identification of Potential Urinary Metabolite Biomarkers of Pseudomonas aeruginosa Ventilator-Associated Pneumonia

Abstract

Introduction: Ventilator-associated pneumonia (VAP) caused by Pseudomonas aeruginosa is a major cause of morbidity and mortality in hospital intensive care units (ICU). Rapid identification of P. aeruginosa-derived markers in easily accessible patients' samples can enable an early detection of P. aeruginosa VAP (VAP-PA), thereby stewarding antibiotic use and improving clinical outcomes.

Methods: Metabolites were analysed using liquid chromatography-mass spectrometry (LC-MS) in prospectively collected urine samples from mechanically ventilated patients admitted to the Antwerp University Hospital ICU. Patients were followed from the start of mechanical ventilation (n = 100 patients) till the time of clinical diagnosis of VAP (n = 13). Patients (n = 8) in whom diagnosis of VAP was further confirmed by culturing respiratory samples and urine samples were studied for semi-quantitative metabolomics.

Results: We first show that multivariate analyses highly discriminated VAP-PA from VAP-non-PA as well as from the pre-infection groups (R ² = .97 and .98, respectively). A further univariate analysis identified 58 metabolites that were significantly elevated or uniquely present in VAP-PA compared to the VAP-non-PA and pre-infection groups (P < .05). These comprised both a known metabolite of histidine as well as a novel nicotine metabolite. Most interestingly, we identified 3 metabolites that were not only highly upregulated for, but were also highly specific to, VAP-PA, as these metabolites were completely absent in all pre-infection timepoints and in VAP-non-PA group.

Conclusions: Considerable differences exist between urine metabolites in VAP-PA compared to VAP due to other bacterial aetiologies as well to non-VAP (pre-infection) timepoints. The unique urinary metabolic biomarkers we describe here, if further validated, could serve as highly specific diagnostic biomarkers of VAP-PA.

Keywords: Hospital-acquired pneumonia; Pseudomonas aeruginosa; VAP; mass spectrometry; metabolomics; urine biomarkers.

Figure 1.

Flow diagram of study design, and patient inclusion and sampling.

Figure 2.

Clustering of VAP patients based on aetiology. Heatmaps for positive electron spray ionisation (left panel) and negative electron spray ionisation (right panel). The heatmaps were constructed based on the top 50 discriminating metabolites using MetaboAnalyst and was based on supervised hierarchical clustering of patients with different aetiologies at presumptive diagnosis of VAP. The discriminating features numbered 1 through 50 on Y axis are listed in Supplemental Information, SI Table 2.

Figure 3.

Multivariate analysis shows clear separation of VAP-PA versus VAP–non-PA and pre-infection time-point. Partial Least Square Discriminant Analysis (PLS-DA) shows clear separation of VAP-PA compared to VAP–non-PA and the pre-infection time-points for both positive and negative ionisation modes (Pos ESI, Neg ESI) (R² _{Pos ESI} = 0.96744; R² _{Neg ESI} = 0.97671).

Figure 4.

Clustering of VAP patient urine samples by OPLS analyses. (A) Orthogonal Projection to Latent Structures (OPLS) for the separation of spectra from patient urine collected at pre-infection timepoint and at presumptive diagnosis of VAP timepoint. Analyses were performed for datasets of both positive and negative ionisation mode (Pos ESI, Neg ESI) and the shaded area enclosing each group represents 95% confidence interval. (B) S-plots constructed from the supervised OPLS analysis of Pos ESI and Neg ESI respectively. Metabolites with the highest abundance and correlation in the VAP-PA samples populate the upper right quadrant, whereas metabolites with the lowest abundance and correlation in the VAP-PA group are residing in the lower left-hand quadrant.

Figure 5.

Top discriminating metabolites in univariate analyses. (A) Top 3 targets exclusively present in VAP caused by P. aeruginosa. Data is represented as integrated ion intensity extracted through LC-MS data. (B) Top 2 targets exclusively present in P. aeruginosa and S. marcescens VAP. (C) 1,3-dipropyl-6-aminouracyl showed increased excretion in Gram-negative (G−) VAP and was absent in gram-positive (G+) and pre-infection control samples. A–C, data is represented as integrated ion intensity extracted through LC-MS data. P < .05 in A–B indicates significance between VAP-PA and all other samples and in C indicates significance between Gram-negative VAP and all other samples. Statistical differences were calculated using Mann–Whitney test.

Figure 6.

Identified metabolites showing increased excretion in P. aeruginosa VAP patients. (A) MS/MS identified glucuronidated targets showing increased excretion in VAP-PA. (B) 3-succinoylpyridine showed increased excretion in all VAP patients, although increase in excretion was remarkably higher in VAP-PA urine samples. A–B, data is represented as integrated ion intensity extracted through LC-MS data. P < .05 indicates significance between VAP-PA and all other samples. Statistical differences were calculated using Mann–Whitney test. Abbreviations: EC, E. coli; PA, P. aeruginosa; SA: S. aureus; SE, S. epidermidis; SM, S. marcescens.