Deciphering Bacterial Community Structure, Functional Prediction and Food Safety Assessment in Fermented Fruits Using Next-Generation 16S rRNA Amplicon Sequencing

Abstract

Fermented fruits and vegetables play an important role in safeguarding food security world-wide. Recently, robust sequencing-based microbial community analysis platforms have improved microbial safety assessment. This study aimed to examine the composition of bacteria and evaluate the bacterial safety of fermented fruit products using high-throughput 16S-rRNA metagenomic analysis. The operational taxonomic unit-based taxonomic classification of DNA sequences revealed 53 bacterial genera. However, the amplicon sequencing variant (ASV)-based clustering revealed 43 classifiable bacterial genera. Taxonomic classifications revealed that the abundance of Sphingomonas, which was the predominant genus in the majority of tested samples, was more than 85–90% among the total identified bacterial community in most samples. Among these identified genera, 13 low abundance genera were potential opportunistic pathogens, including Acinetobacter, Bacillus, Staphylococcus, Clostridium, Klebsiella, Mycobacterium, Ochrobactrum, Chryseobacterium, Stenotrophomonas, and Streptococcus. Of these 13 genera, 13 major opportunistic pathogenic species were validated using polymerase chain reaction. The pathogens were not detected in the samples of different stages and the final products of fermentation, except in one sample from the first stage of fermentation in which S. aureus was detected. This finding was consistent with that of ASV-based taxonomic classification according to which S. aureus was detected only in the sample from the first stage of fermentation. However, S. aureus was not significantly correlated with the human disease pathways. These results indicated that fermentation is a reliable and safe process as pathogenic bacteria were not detected in the fermentation products. The hybrid method reported in this study can be used simultaneously to evaluate the bacterial diversity, their functional predictions and safety assessment of novel fermentation products. Additionally, this hybrid method does not involve the random detection of pathogens, which can markedly decrease the time of detection and food safety verification. Furthermore, this hybrid method can be used for the quality control of products and the identification of external contamination.

Fermentation quality, bacterial community, and predicted functional profiles in silage prepared with alfalfa, perennial ryegrass and their mixture in the karst region

“…As shown in Figure 4A , a total of six different metabolic pathways were observed in all the silages. The main predicted genes were assigned to metabolism, accounting for >76% in all the silages, followed by environmental information processing, genetic information processing and cellular processes, which is in accordance with a previous report ( Hussain et al, 2021 ). After 7 days of ensiling, the 30% MS + 70% LP and 100% LP treatments had lower abundances of “Environmental information processing,” “Cellular processes” and “Human diseases” and higher abundances of “Metabolism,” “Genetic information processing” and “Organismal systems” than the other treatments.…”

The Evaluation of Bacterial Abundance and Functional Potentials in the Three Major Watersheds, Located in the Hot Spring Zone of the Tatun Volcano Group Basin, Taiwan

“…Amplicon sequence variants (ASVs) were picked using DADA2 after filtering the chimeric sequences, marginal sequence errors, and noisy sequences. Furthermore, the taxonomy classification was done using the SILVA reference database [ 22 ]. The relative abundance of the microbial community associated with each of the samples was obtained using the QIIME2 view.…”

Assessment of Temporal Effects of a Mud Volcanic Eruption on the Bacterial Community and Their Predicted Metabolic Functions in the Mud Volcanic Sites of Niaosong, Southern Taiwan

“…The raw sequence files from the NGS platform were then processed using the Quantitative Insights into Microbial Ecology (QIIME2) pipeline. After removing the chimeric, noisy sequences, and marginal sequence errors, amplicon sequence variants (ASVs) were picked using DADA2, and further taxonomy classification was done using the SILVA reference database [20]. The relative abundance of the microbial community associated with each sample was obtained using the QIIME2 view [21].…”