Draft Genome Sequence of Serratia proteamaculans MFPA44A14-05, a Model Organism for the Study of Meat and Seafood Spoilage

Genomic diversity of Serratia proteamaculans and Serratia liquefaciens predominant in seafood products and spoilage potential analyses

Serratia sp. cause food losses and waste due to spoilage; it is noteworthy that they represent a dominant population in seafood. The main spoilage associated species comprise S. liquefaciens, S. grimesii, S. proteamaculans and S. quinivorans, also known as S. liquefaciens-like strains. These species are difficult to discriminate since classical 16S rRNA gene-based sequences do not possess sufficient resolution. In this study, a phylogeny based on the short-length luxS gene was able to speciate 47 Serratia isolates from seafood, with S. proteamaculans being the main species from fresh salmon and tuna, cold-smoked salmon, and cooked shrimp while S. liquefaciens was only found in cold-smoked salmon. The genome of the first S. proteamaculans strain isolated from the seafood matrix (CD3406 strain) was sequenced. Pangenome analyses of S. proteamaculans and S. liquefaciens indicated high adaptation potential. Biosynthetic pathways involved in antimicrobial compounds production and in the main seafood spoilage compounds were also identified. The genetic equipment highlighted in this study contributed to gain further insights into the predominance of Serratia in seafood products and their capacity to spoil.

Deciphering intra-species bacterial diversity of meat and seafood spoilage microbiota using gyrB amplicon sequencing: A comparative analysis with 16S rDNA V3-V4 amplicon sequencing

Meat and seafood spoilage ecosystems harbor extensive bacterial genomic diversity that is mainly found within a small number of species but within a large number of strains with different spoilage metabolic potential. To decipher the intraspecies diversity of such microbiota, traditional metagenetic analysis using the 16S rRNA gene is inadequate. We therefore assessed the potential benefit of an alternative genetic marker, gyrB, which encodes the subunit B of DNA gyrase, a type II DNA topoisomerase. A comparison between 16S rDNA-based (V3-V4) amplicon sequencing and gyrB-based amplicon sequencing was carried out in five types of meat and seafood products, with five mock communities serving as quality controls. Our results revealed that bacterial richness in these mock communities and food samples was estimated with higher accuracy using gyrB than using16S rDNA. However, for Firmicutes species, 35% of putative gyrB reads were actually identified as sequences of a gyrB paralog, parE, which encodes subunit B of topoisomerase IV; we therefore constructed a reference database of published sequences of both gyrB and pare for use in all subsequent analyses. Despite this co-amplification, the deviation between relative sequencing quantification and absolute qPCR quantification was comparable to that observed for 16S rDNA for all the tested species. This confirms that gyrB can be used successfully alongside 16S rDNA to determine the species composition (richness and evenness) of food microbiota. The major benefit of gyrB sequencing is its potential for improving taxonomic assignment and for further investigating OTU richness at the subspecies level, thus allowing more accurate discrimination of samples. Indeed, 80% of the reads of the 16S rDNA dataset were represented by thirteen 16S rDNA-based OTUs that could not be assigned at the species-level. Instead, these same clades corresponded to 44 gyrB-based OTUs, which differentiated various lineages down to the subspecies level. The increased ability of gyrB-based analyses to track and trace phylogenetically different groups of strains will generate improved resolution and more reliable results for studies of the strains implicated in food processes.

Extended antibiotic treatment in salmon farms select multiresistant gut bacteria with a high prevalence of antibiotic resistance genes

The high use of antibiotics for the treatment of bacterial diseases is one of the main problems in the mass production of animal protein. Salmon farming in Chile is a clear example of the above statement, where more than 5,500 tonnes of antibiotics have been used over the last 10 years. This has caused a great impact both at the production level and on the environment; however, there are still few works in relation to it. In order to demonstrate the impact of the high use of antibiotics on fish gut microbiota, we have selected four salmon farms presenting a similar amount of fish of the Atlantic salmon species (Salmo salar), ranging from 4,500 to 6,000 tonnes. All of these farms used treatments with high doses of antibiotics. Thus, 15 healthy fish were selected and euthanised in order to isolate the bacteria resistant to the antibiotics oxytetracycline and florfenicol from the gut microbiota. In total, 47 bacterial isolates resistant to florfenicol and 44 resistant to oxytetracycline were isolated, among which isolates with Minimum Inhibitory Concentrations (MIC) exceeding 2048 μg/mL for florfenicol and 1024 μg/mL for oxytetracycline were found. In addition, another six different antibiotics were tested in order to demonstrate the multiresistance phenomenon. In this regard, six isolates of 91 showed elevated resistance values for the eight tested antibiotics, including florfenicol and oxytetracycline, were found. These bacteria were called “super-resistant” bacteria. This phenotypic resistance was verified at a genotypic level since most isolates showed antibiotic resistance genes (ARGs) to florfenicol and oxytetracycline. Specifically, 77% of antibiotic resistant bacteria showed at least one gene resistant to florfenicol and 89% showed at least one gene resistant to oxytetracycline. In the present study, it was demonstrated that the high use of the antibiotics florfenicol and oxytetracycline has, as a consequence, the selection of multiresistant bacteria in the gut microbiota of farmed fish of the Salmo salar species at the seawater stage. Also, the phenotypic resistance of these bacteria can be correlated with the presence of antibiotic resistance genes.