Application of shotgun metagenomics to smoked salmon experimentally spiked: Comparison between sequencing and microbiological data using different bioinformatic approaches

De Cesare, Alessandra; Oliveri, Chiara; Lucchi, Alex; Pasquali, Frederique; Manfreda, Gerardo

The aims of this study were i) to evaluate the possibility to detect and possibly quantify microorganisms belonging to different domains experimentally spiked in smoked salmon at known concentrations using shotgun metagenomics; ii) to compare the sequencing results using four bioinformatic tools. The salmon was spiked with six species of bacteria, including potential foodborne pathogens, as well as Cryptosporidium parvum, Saccharomyces cerevisiae and Bovine alphaherpesvirus 1. After spiking, the salmon was kept refrigerated before DNA extraction, library preparation and sequencing at 7 Gbp in paired ends at 150 bp. The bioinformatic tools named MG-RAST, OneCodex, CosmosID and MgMapper were used for the sequence analysis and the data provided were compared using STAMP. All bacteria spiked in the salmon were identified using all bioinformatic tools. Such tools were also able to assign the higher abundances to the species Propionibacterium freudenreichii spiked at the highest concentration in comparison to the other bacteria. Nevertheless, different abundances were quantified for bacteria spiked in the salmon at the same cell concentration. Cryptosporidium parvum was detected by all bioinformatics tools, while Saccharomyces cerevisiae by MG-RAST only. Finally, the DNA virus was detected by CosmosID and OneCodex only. Overall, the results of this study showed that shotgun metagenomics can be applied to detect microorganisms belonging to different domains in the same food sample. Nevertheless, a direct correlation between cell concentration of each spiked microorganism and number of corresponding reads cannot be established yet.

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De Cesare, Alessandra / Oliveri, Chiara / Lucchi, Alex / et al: Application of shotgun metagenomics to smoked salmon experimentally spiked: Comparison between sequencing and microbiological data using different bioinformatic approaches. 2019.

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