Recommendations for identifying pathogenic Vibrio spp. as part of disease surveillance programmes in recirculating aquaculture systems for Pacific white shrimps (Litopenaeus vannamei).
Julia BauerFelix TeitgeLisa NeffeMikołaj AdamekArne JungChristina PepplerDieter SteinhagenVerena Jung-SchroersPublished in: Journal of fish diseases (2018)
Due to their pathogenic potential, identifying Vibrio species from recirculating aquaculture systems (RAS) for Pacific white shrimp (Litopenaeus vannamei) is of great importance to determine the risk for animal's as well as for the consumer's health. The present study compared identification results for a total of 93 Vibrio isolates, including type strains and isolates from shrimp aquaculture. Results from biochemical identifications, 16S rRNA sequencing, sequencing of the uridylate kinase encoding gene pyrH and analysis of the protein spectra assessed by MALDI-TOF MS were compared. The results achieved by these different methods were highly divergent for many of the analysed isolates and for several Vibrio spp difficulties in reliably identifying occurred. These difficulties mainly resulted from missing entries in digital databases, a low number of comparable isolates analysed so far, and high interspecific similarities of biochemical traits and nucleotide sequences between the closely related Vibrio species. Due to the presented data, it can be concluded that for identifying Vibrio spp. from samples in routine diagnostics, it is recommended to use MALDI-TOF MS analysis for a quick and reliable identification of pathogenic Vibrio sp. Nevertheless, editing the database, containing the main spectra of Vibrio is recommended to achieve reliable identification results.
Keyphrases
- biofilm formation
- healthcare
- public health
- mass spectrometry
- genetic diversity
- escherichia coli
- staphylococcus aureus
- mental health
- genome wide
- emergency department
- candida albicans
- density functional theory
- machine learning
- small molecule
- transcription factor
- cystic fibrosis
- human health
- deep learning
- genome wide analysis