Milk microbiota has a great influence on the safety and quality of dairy products. However, few studies have investigated the variations of bacterial composition in raw milk. In this study, raw milk samples were collected in 12 successive months, and their bacterial compositions were determined by 16 S rRNA gene sequencing. The highest diversity of bacterial composition was detected in June, while the lowest was in December. Firmicutes, Proteobacteria and Actinobacteria were the most abundant phyla and exhibited a counter-balanced relationship. Pseudomonas, Lactococcus and Acinetobacter were the most prevalent genera (>1%), and a tiny core microbiota (Acinetobacter and Pseudomonas) was observed. Temperature and humidity were the determining factors for most variation in bacterial compositions at both the phylum and genus levels. Higher abundances of Pseudomonas, Propionibacterium and Flavobacterium were correlated with low temperature. Furthermore, Pseudomonas/Propionibacterium and Lactobacillus/Bifidobacterium were two pairs of genera that had synergistic effects. Associations between the microbiota and milk quality parameters were analyzed. The abundances of Propionibacterium and Pseudoalteromonas were negatively correlated to total bacterial count, which meant that they helped to maintain milk quality, while a series of environmental microorganisms contributed to the spoilage of raw milk.
A mPCR assay targeting adnA and fliC genes showed rapid and reliable detection of P. fluorescens with biofilm formation ability, which could be useful to detect this contamination in milk samples.
Fermentative bacteria, the main microbiota in yogurt, interfere with the detection of unintended bacterial contaminants. The removal of fermentative bacteria and enrichment of unintended bacterial contaminants is a challenging task in bacterial detection. The present study developed a new 16S rRNA-depletion PCR for such enrichment and detection. Specifically, a singleguide RNA was designed and synthesized based on the 16S rRNA sequence of Streptococcus thermophilus, with the highest DNA abundance in the yogurt. The CRISPR-Cas9 system was used to specifically cleave and remove the genomic DNA of the fermentative bacteria, followed by PCR amplification. This method improved the detection sensitivity, simplified the operation steps, and reduced the detection cost of PCR analysis. We also used the 16S rRNA-depletion PCR to amplify and detect the unintended bacterial contaminants in yogurts with shrunken packages and analyzed the underlying reasons to prevent this issue of product quality.
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