Rapid and Reproducible MALDI-TOF-Based Method for the Detection of Vancomycin-Resistant Enterococcus faecium Using...

Area of Science:

• Clinical Microbiology
• Infectious Diseases
• Mass Spectrometry

Background:

• Vancomycin-resistant Enterococcus faecium (VRE) is a significant healthcare-associated pathogen due to its increasing prevalence and resistance to antibiotics.
• Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is a powerful tool for bacterial identification, but its application for antimicrobial resistance detection, particularly for VRE, requires further evaluation.
• Accurate and rapid detection methods are crucial for controlling VRE outbreaks and guiding patient treatment.

Purpose of the Study:

• To assess the repeatability of MALDI-TOF MS for protein peak analysis in Enterococcus faecium.
• To evaluate the performance of MALDI-TOF MS in discriminating between vancomycin-susceptible (VSE) and vancomycin-resistant (VRE) isolates.
• To investigate the potential of MALDI-TOF MS for differentiating specific VRE types (VanA and VanB).

Main Methods:

• Protein mass spectra were acquired from 178 unique clinical Enterococcus faecium isolates (92 VSE, 31 VanA VRE, 55 VanB VRE) using MALDI-TOF MS and processed with Clover MS Data Analysis software.
• Technical and biological repeatability of MALDI-TOF MS peak analysis was assessed.
• Unsupervised (Principal Component Analysis) and supervised machine learning algorithms (Support Vector Machine, Random Forest, Partial Least Squares-Discriminant Analysis) were employed for spectral data analysis and isolate classification.

Main Results:

• Repeatability analysis indicated lower variability for normalized MALDI-TOF MS data, particularly for peaks within the 3000-9000 m/z range.
• Supervised algorithms achieved VSE vs. VRE discrimination rates of 80.9% (SVM), 79.2% (RF), and 77.5% (PLS-DA).
• Support Vector Machine (SVM) correctly differentiated VanA from VanB VRE isolates in 86.6% of cases.

Conclusions:

• MALDI-TOF MS, coupled with appropriate peak analysis and machine learning algorithms, demonstrates potential as a rapid and effective tool for screening vancomycin resistance in Enterococcus faecium.
• The method showed good performance in distinguishing VSE from VRE and differentiating between VanA and VanB VRE types.
• Further optimization and validation are necessary to enhance the accuracy and clinical utility of MALDI-TOF MS for routine VRE detection.