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

Yamin Liu1, Junwen Cui2, Chunhua Qie1

  • 1Clinical School of the Second People's Hospital, Tianjin Medical University, Tianjin 300192, China.

Computational and Mathematical Methods in Medicine
|June 27, 2022
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Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) effectively identifies and clusters clinical Bordetella species. Expanding the MALDI-TOF MS database enhances accuracy for epidemiological and taxonomic research of this pathogen.

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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.