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Ye J, Guo J, Li T, Tian J, Yu M, Wang X, Majeed U, Song W, Xiao J, Luo Y, Yue T. Phage-based technologies for highly sensitive luminescent detection of foodborne pathogens and microbial toxins: A review. Compr Rev Food Sci Food Saf 2022; 21:1843-1867. [PMID: 35142431 DOI: 10.1111/1541-4337.12908] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 12/25/2021] [Accepted: 12/28/2021] [Indexed: 02/05/2023]
Abstract
Foodborne pathogens and microbial toxins are the main causes of foodborne illness. However, trace pathogens and toxins in foods are difficult to detect. Thus, techniques for their rapid and sensitive identification and quantification are urgently needed. Phages can specifically recognize and adhere to certain species of microbes or toxins due to molecular complementation between capsid proteins of phages and receptors on the host cell wall or toxins, and thus they have been successfully developed into a detection platform for pathogens and toxins. This review presents an update on phage-based luminescent detection technologies as well as their working principles and characteristics. Based on phage display techniques of temperate phages, reporter gene detection assays have been designed to sensitively detect trace pathogens by luminous intensity. By the host-specific lytic effects of virulent phages, enzyme-catalyzed chemiluminescent detection technologies for pathogens have been exploited. Notably, these phage-based luminescent detection technologies can discriminate viable versus dead microbes. Further, highly selective and sensitive immune-based assays have been developed to detect trace toxins qualitatively and quantitatively via antibody analogs displayed by phages, such as phage-ELISA (enzyme-linked immunosorbent assay) and phage-IPCR (immuno-polymerase chain reaction). This literature research may lead to novel and innocuous phage-based rapid detection technologies to ensure food safety.
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Affiliation(s)
- Jianming Ye
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Jiaqing Guo
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Tairan Li
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Jiaxin Tian
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Mengxi Yu
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Xiaochen Wang
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Usman Majeed
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Wei Song
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Jianbo Xiao
- Department of Analytical Chemistry and Food Science, Faculty of Food Science and Technology, University of Vigo-Ourense Campus, Ourense, Spain
| | - Yane Luo
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China.,Laboratory of Nutritional and Healthy Food-Individuation Manufacturing Engineering, Xi'an, Shaanxi, China.,Research Center of Food Safety Risk Assessment and Control, Xi'an, Shaanxi, China
| | - Tianli Yue
- College of Food Science and Technology, Northwest University, Xi'an, Shaanxi, China.,Laboratory of Nutritional and Healthy Food-Individuation Manufacturing Engineering, Xi'an, Shaanxi, China.,Research Center of Food Safety Risk Assessment and Control, Xi'an, Shaanxi, China
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