Amplicon sequencing for the quantification of spoilage microbiota in complex foods including bacterial spores

Abstract

Abstract

Background Spoilage of food products is frequently caused by bacterial spores and lactic acid bacteria. Identification of these organisms by classic cultivation methods is limited by their ability to form colonies on nutrient agar plates. In this study, we adapted and optimized 16S rRNA amplicon sequencing for quantification of bacterial spores in a canned food matrix and for monitoring the outgrowth of spoilage microbiota in a ready-to-eat food matrix.

Results The detection limit of bar-coded 16S rRNA amplicon sequencing was determined for the number of bacterial spores in a canned food matrix. Analysis of samples from a canned food matrix spiked with a mixture of equinumerous spores from the thermophiles, Geobacillus stearothermophilus and Geobacillus thermoglucosidans, and the mesophiles, Bacillus sporothermodurans, Bacillus cereus, and Bacillus subtilis, led to the detection of these spores with an average limit of 2 × 102 spores ml−1. The data were normalized by setting the number of sequences resulting from DNA of an inactivated bacterial species, present in the matrix at the same concentration in all samples, to a fixed value for quantitative sample-to-sample comparisons. The 16S rRNA amplicon sequencing method was also employed to monitor population dynamics in a ready-to-eat rice meal, incubated over a period of 12 days at 7 °C. The most predominant outgrowth was observed by the genera Leuconostoc, Bacillus, and Paenibacillus. Analysis of meals pre-treated with weak acids showed inhibition of outgrowth of these three genera. The specificity of the amplicon synthesis was improved by the design of oligonucleotides that minimize the amplification of 16S rRNA genes from chloroplasts originating from plant-based material present in the food.

Conclusion This study shows that the composition of complex spoilage populations, including bacterial spores, can be monitored in complex food matrices by bar-coded amplicon sequencing in a quantitative manner. In order to allow sample-to-sample comparisons, normalizations based on background DNA are described. This method offers a solution for the identification and quantification of spoilage microbiota, which cannot be cultivated under standard laboratory conditions. The study indicates variable detection limits among species of bacterial spores resulting from differences in DNA extraction efficiencies.