Explorative Multivariate Analyses of 16S rRNA Gene Data from Microbial Communities in Modified-Atmosphere-Packed Salmon and Coalfish

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Abstract
Modified-atmosphere packaging (MAP) of foods in combination with low-temperature storage extends product shelf life by limiting microbial growth. We investigated the microbial biodiversity of MAP salmon and coalfish by using an explorative approach and analyzing both the total amounts of bacteria and the microbial group composition (both aerobic and anaerobic bacteria). Real-time PCR analyses revealed a surprisingly large difference in the microbial loads for the different fish samples. The microbial composition was determined by examining partial 16S rRNA gene sequences from 180 bacterial isolates, as well as by performing terminal restriction fragment length polymorphism analysis and cloning 92 sequences from PCR products of DNA directly retrieved from the fish matrix. Twenty different bacterial groups were identified. Partial least-squares (PLS) regression was used to relate the major groups of bacteria identified to the fish matrix and storage time. A strong association of coalfish with Photobacterium phosphoreum was observed. Brochothrix spp. and Carnobacterium spp., on the other hand, were associated with salmon. These bacteria dominated the fish matrixes after a storage period. Twelve Carnobacterium isolates were identified as either Carnobacterium piscicola (five isolates) or Carnobacterium divergens (seven isolates), while the eight Brochothrix isolates were identified as Brochothrix thermosphacta by full-length 16S rRNA gene sequencing. Principal-component analyses and PLS analysis of the growth characteristics (with 49 different substrates) showed that C. piscicola had distinct substrate requirements, while the requirements of B. thermosphacta and C. piscicola were quite divergent. In conclusion, our explorative multivariate approach gave a picture of the total microbial biodiversity in MAP fish that was more comprehensive than the picture that could be obtained previously. Such information is crucial in controlled food production when, for example, the hazard analysis of critical control points principle is used.

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