Identification and classification of seafood-borne pathogenic and spoilage bacteria: 16S rRNA sequencing versus MALDI-TOF MS fingerprinting
The present study aims to compare two molecular technologies, 16S rRNA sequencing and MALDI‐TOF MS, for bacterial species identification in seafood. With this aim, 70 reference strains from culture collections, including important seafood‐borne pathogenic and spoilage bacterial species, and 50 strai...
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Veröffentlicht in: | Electrophoresis 2013-03, Vol.34 (6), p.877-887 |
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Zusammenfassung: | The present study aims to compare two molecular technologies, 16S rRNA sequencing and MALDI‐TOF MS, for bacterial species identification in seafood. With this aim, 70 reference strains from culture collections, including important seafood‐borne pathogenic and spoilage bacterial species, and 50 strains isolated from commercial seafood products, were analysed by both techniques. Genomic analysis only identified the species of 50% of the isolated strains, proving to be particularly poor at identifying members of the Pseudomonas and Bacillus genera. In contrast, MALDI‐TOF MS fingerprinting identified 76% of the strains at the species level. The mass spectral data were submitted to the SpectraBank database (http://www.spectrabank.org), making this information available to other researchers. Furthermore, cluster analysis of the peak mass lists was carried out with the web application SPECLUST and the calculated groupings were consistent with results determined by a phylogenetic approach that is based on the 16S rRNA sequences. However, the MALDI‐TOF MS analysis demonstrated more discriminating potential that allowed for better classification, especially for the Pseudomonas and Bacillus genera. This is of importance with respect to the varying pathogenic and spoilage character at the intragenus and intraspecies level. In this sense, MALDI‐TOF MS demonstrated to be a competent bacterial typing tool that extends phenotypic and genotypic approaches, allowing a more ample classification of bacterial strains. |
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ISSN: | 0173-0835 1522-2683 |
DOI: | 10.1002/elps.201200532 |