The potentially pathogenic species of the genus Vibrio pose a threat to both humans and animals, creating medical burdens and economic losses to the mariculture industry. Improvements in surveillance and diagnosis are needed to successfully manage vibriosis outbreaks. Matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) can provide rapid diagnosis and has been widely used in the identification of Vibrio spp. The main weakness of this technology is the limited number of strains and species of Vibrio in the existing commercial database. Here, we develop a new in-house database named PVBase containing 790 main spectra projections (MSP) of ten Vibrio species that come from various regions of China and include abundant clinical and environmental strains. PVBase was validated through a blind test of 65 Vibrio strains. The identification accuracy and scoring of Vibrio strains was greatly improved through the addition of PVBase. Identification accuracy increased from 73.4 to 100%. The number of strains with identification scores above 2.2 increased from 53.1% to 96.9% and 53.1% of strains had an identification score above 2.59. Moreover, perfect discrimination was obtained when using all of the MSPs created for the Vibrio species, even for very closely related species such as V. cholerae, V. albensis, and V. mimicus or V. alginolyticus, V. parahaemolyticus, and V. harveyi. In addition, we used phyloproteomic analysis to study whether there are differences in protein fingerprints of different regions or pathogenic strains. We found that MSP characteristics of Vibrio species were not related to their region or source. With the construction of PVBase, the identification efficiency of potentially pathogenic Vibrio species has been greatly improved, which is an important advance for epidemic prevention and control, and aquaculture disease detection.
Keywords: Vibrio species; clinical strain; database; environmental strain; pathogenic; regional strain.
Copyright © 2022 Liu, Kang, Xu, Wang, Gao, Li, Li, Yuan, Yuan, Wang, Zhao and Xin.