Species-level discrimination of psychrotrophic pathogenic and spoilage gram-negative raw milk isolates using a combined MALDI-TOF MS proteomics-bioinformatics-based approach

Identification of psychrotrophic pathogenic and spoilage Gram-negative bacteria using rapid and reliable techniques is important in commercial milk processing, as these bacteria can produce heat-resistant proteases and act as postprocessing contaminants in pasteurized milk. Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is a proven technology for identification of bacteria in food, however, may require optimization for identification of pathogenic and spoilage bacteria in milk and dairy products. The current study evaluated the effects of various culture conditions and sample preparation methods on assigning of raw milk isolates to the species level by MALDI-TOF MS. The results indicated that culture media, incubation conditions (temperature and time), and sample preparation significantly affected the identification rates of bacteria to the species level. Nevertheless, the development of spectral libraries of isolates grown on different media using a web tool for hierarchical clustering of peptide mass spectra (SPECLUST) followed by a ribosomal protein based bioinformatics approach significantly enhanced the assigning of bacteria, with at least one unique candidate biomarker peak identified for each species. Phyloproteomic relationships based on spectral profiles were compared to phylogenetic analysis using 16S rRNA gene sequences and demonstrated similar clustering patterns with significant discriminatory power. Thus, with appropriate optimization, MALDI-TOF MS is a valuable tool for species-level discrimination of pathogenic and milk spoilage bacteria.