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Rapid Discrimination of Pseudomonas aeruginosa ST175 Isolates Involved in a Nosocomial Outbreak Using MALDI-TOF Mass

Ana Candela1,2, Manuel J Arroyo3, María Sánchez-Cueto1,2

  • 1Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

Transboundary and Emerging Diseases
|April 30, 2025
PubMed

Insights

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and Fourier-transform infrared spectroscopy (FTIR-S) show promise for detecting Pseudomonas aeruginosa outbreaks. These methods, combined with machine learning, offer rapid identification of high-risk bacterial clones.

Area of Science:

  • Clinical Microbiology
  • Infectious Disease Diagnostics
  • Spectroscopic Analysis

Background:

  • Nosocomial outbreaks caused by *Pseudomonas aeruginosa* pose significant challenges in healthcare settings.
  • Traditional DNA-based methods for bacterial strain typing can be time-consuming and resource-intensive.
  • Rapid and accurate detection of outbreak-specific bacterial clones is crucial for effective infection control.

Purpose of the Study:

  • To evaluate matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and Fourier-transform infrared spectroscopy (FTIR-S) as rapid diagnostic alternatives.
  • To assess the utility of these methods for detecting *Pseudomonas aeruginosa* sequence type (ST) 175 isolates during a hospital outbreak.
  • To compare the discriminatory power of MALDI-TOF MS and FTIR-S against established methods like pulse-field gel electrophoresis (PFGE) and whole-genome sequencing (WGS).

Main Methods:

  • Analysis of 27 *P. aeruginosa* isolates from a hospital outbreak using MALDI-TOF MS and FTIR-S.
  • Spectra acquisition involved specific sample preparation protocols for each technique.
  • Data analysis utilized Clover Biosoft Software with machine learning algorithms (e.g., K-nearest neighbor) for classification and validation.

Main Results:

  • MALDI-TOF MS identified three outbreak-specific biomarkers (5,169, 6,915, and 7,236 m/z) with discriminatory power comparable to PFGE.
  • A classification model based on these biomarkers achieved 97.0% correct classification, discriminating clusters similar to WGS.
  • FTIR-S demonstrated discriminatory power similar to PFGE and successfully differentiated the analyzed sequence types (STs).

Conclusions:

  • MALDI-TOF MS and FTIR-S, when combined with machine learning tools, are effective alternatives to DNA-based methods for outbreak detection.
  • These spectroscopic techniques offer a powerful approach for real-time monitoring of high-risk bacterial clones in healthcare environments.
  • The study highlights the potential for rapid, cost-effective identification of pathogens involved in nosocomial infections.

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