Automatic Identification of MALDI-TOF MS Database Using Classical Bordetella Species Isolates

Area of Science:

• Microbiology
• Mass Spectrometry
• Bacteriology

Background:

• Accurate identification and clustering of clinical Bordetella species are crucial for epidemiological and taxonomic research.
• Conventional methods for identifying fastidious bacteria like Bordetella can be time-consuming and labor-intensive.

Purpose of the Study:

• To evaluate and expand the automatic identification and clustering capabilities for clinical Bordetella species using MALDI-TOF MS.
• To assess the robustness of an expanded MALDI-TOF MS database for Bordetella identification.

Main Methods:

• Whole-gene sequencing was used to identify 28 initial Bordetella strains.
• MALDI-TOF MS was employed to analyze these strains, and the resulting spectra were added to the manufacturer's database.
• The expanded database was then used to identify 91 clinical Bordetella isolates, with data visualized using MALDI Biotyper 3.1 software for clustering.

Main Results:

• The expanded MALDI-TOF MS database achieved 100% genus-level identification and 93.4% species-level identification for 91 clinical isolates.
• Proteomics analysis using MALDI-TOF MS clustered the isolates into two main groups, with 97.8% belonging to cluster II.
• Mass spectrometry-based phylogenetic trees accurately reflected the classification of the three clinical Bordetella species.

Conclusions:

• MALDI-TOF MS offers a rapid, efficient, and accurate alternative for the identification and clustering of difficult-to-culture Bordetella species.
• Expanding the MALDI-TOF MS database significantly improves the identification accuracy for clinical Bordetella isolates.
• This approach facilitates epidemiological and taxonomic studies of Bordetella, a significant bacterial pathogen.