Microbiota Analysis for the Optimization of Campylobacter Isolation From Chicken Carcasses Using Selective Media

• Published in: Frontiers in microbiology 2019-06, Vol.10, p.1381-1381
• Main Authors: Kim, Jinshil, Shin, Hakdong, Park, Hyeeun, Jung, Hayan, Kim, Junhyung, Cho, Seongbeom, Ryu, Sangryeol, Jeon, Byeonghwa
• Format: Article
• Language: English
• Subjects: 16S rRNA sequencing; bacterial isolation; Campylobacter; Microbiology; microbiota compositions; selective enrichment
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2019.01381

Since contaminated poultry meat is the major source of transmitting to humans, the isolation of from poultry carcasses is frequently performed in many countries as a baseline survey to ensure food safety. However, existing isolation methods have technical limitations in isolating this fastidious bacterium, such as a growth competition with indigenous bacteria in food samples. In this study, we compared the differences in microbiota compositions between Bolton and Preston selective media, two most common selective media to isolate , and investigated how different microbiota compositions resulting from different enrichment methods may affect isolation frequencies. A next-generation sequencing (NGS) analysis of 16S demonstrated that Bolton and Preston-selective enrichments generated different microbiota structures that shared only 31.57% of Operating Taxonomic Unit (OTU) types. Particularly, was highly prevalent in Bolton selective media, and the enrichment cultures that increase negatively affected the efficacy of r isolation. Furthermore, the combination of the selective media made a significant difference in the isolation frequency. The Bolton broth and Preston agar combination exhibited the highest (60.0%) frequencies of isolation, whereas the Bolton broth and Bolton agar combination showed the lowest (2.5%). These results show that each selective medium generates a unique microbiota structure and that the sequence of combining the selective media also critically affects the isolation frequency by altering microbiota compositions. In this study, we demonstrated how a microbiota analysis using NGS can be utilized to optimize a protocol for bacterial isolation from food samples.