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Leaf-associated microbiota on perilla (Perilla frutescens var. frutescens) cultivated in South Korea to detect the potential risk of food poisoning

Author:
Jeon, Da-young, Yum, Su-jin, Seo, Dong Woo, Kim, Seung Min, Jeong, Hee Gon
Source:
Food research international 2019 v.126 pp. 108664
ISSN:
0963-9969
Subject:
Acinetobacter lwoffii, Klebsiella pneumoniae, Perilla frutescens var. frutescens, Sphingomonas, Staphylococcus aureus, bacterial communities, food safety, foodborne illness, genes, leaves, microbial load, microbiome, microorganisms, pathogens, prediction, quantitative polymerase chain reaction, reverse transcriptase polymerase chain reaction, ribosomal RNA, South Korea
Abstract:
Perilla (Perilla frutescens) is a commonly consumed herb with various health benefits in Asia. However, the risks of food-borne illness owing to the presence of pathogens on perilla leaves have not been evaluated. In this study, we evaluated the microbiota of perilla leaves harvested in South Korea using Illumina MiSeq sequencing of the 16S rRNA gene. In total, 2,743,003 sequencing reads were obtained, and 92–437 operational taxonomic units were observed in all samples. Bacterial loads were quantified, and the diversity indices were compared. Differences in the microbiota among sampling times and regions were also investigated. Proteobacteria and Firmicutes were predominant phyla at both times. At the class level, the bacterial communities were composed primarily of Alphaproteobacteria, Bacilli, and Gammaproteobacteria. Diverse bacterial taxa, such as Bacillus, uncultured family Enterobacteriaceae, and Sphingomonas were detected, and the representative pathogenic species (i.e., Acinetobacter lwoffii, Klebsiella pneumoniae, and Staphylococcus aureus) were quantified by qRT-PCR. The results of the co-occurrence network analysis showed characteristics of bacterial taxa in the microbiome on perilla leaves and provided insights into the roles of correlations among diverse microbes, including potential pathogens. Based on these results, the potential risk of food-borne illness from consumption of perilla leaves may be higher in July than in April. In summary, the microbial compositions determined in this study can be used as a base data for food-safety management for prediction and prevention of future outbreaks.
Agid:
6699269