Introduction . Accurate and rapid identification of marine bacteria is essential for the precise diagnosis and treatment of infectious diseases caused by marine pathogens. The accuracy of microbial identification using matrix-assisted laser desorption/ionization time-of-flight MS (MALDI-TOF MS) primarily depends on the diversity and number of strains included in the reference database. Hypothesis/Gap statement . We hypothesized that expanding the MALDI-TOF MS database with a broader collection of protein spectral profiles from marine bacteria would significantly enhance identification accuracy for these strains. Aim . This study aimed to establish a marine bacterial database to improve identification accuracy and to evaluate the applicability of MALDI-TOF MS-based cluster analysis for tracing the origin of clinical strains. Methodology . We collected 203 strains isolated from marine environments and clinical samples, acquired their MALDI-TOF MS spectra and constructed a mass spectral database specific to marine bacteria. To validate the accuracy of the expanded database, 80 external strains were subsequently tested. Furthermore, we assessed the strain classification efficacy of MALDI-TOF MS cluster analysis and phylogenetic trees constructed from gene sequences. Results . The species-level identification rate increased from 88.75 to 97.5%. The proportion of strains achieving a reliable identification score (>2.3) rose markedly from 43.75 to 91.25%. Cluster analysis based on MALDI-TOF MS demonstrated high accuracy in grouping bacteria at the species level. In addition, the maximum likelihood (ML) phylogenetic tree exhibited significantly higher bootstrap support values compared to the neighbour-joining tree. Conclusion . The expanded marine bacterial database markedly enhances the accuracy and reliability of MALDI-TOF MS for identifying marine pathogens. For species identification and traceability, we recommend a combined strategy that includes initial MALDI-TOF MS screening and verification with phylogenetic trees based on the ML method.
Guo et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: