Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
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Recent Advancements in Technologies to Detect Enterohaemorrhagic Escherichia coli Shiga Toxins

  • Jeongtae Kim (Department of Electronic Engineering, Hanbat National University) ;
  • Jun Bong Lee (College of Veterinary Medicine & Institute of Veterinary Science, Kangwon National University) ;
  • Jaewon Park (Green Manufacturing Research Center, Korea University) ;
  • Chiwan Koo (Department of Electronic Engineering, Hanbat National University) ;
  • Moo-Seung Lee (Environmental Diseases Research Center, Korea Research Institute of Bioscience and Biotechnology)
  • Received : 2022.12.13
  • Accepted : 2023.02.13
  • Published : 2023.05.28
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Abstract

Shiga toxin (Stxs)-producing enterohaemorrhagic Escherichia coli (EHEC) and Shigella dysenteriae serotype 1 are major causative agents of severe bloody diarrhea (known as hemorrhagic colitis) and hemolytic uremic syndrome (HUS) associated with extraintestinal complications such as acute renal failure and neurologic impairment in infected patients under 9 years of age. Extreme nephrotoxicity of Stxs in HUS patients is associated with severe outcomes, highlighting the need to develop technologies to detect low levels of the toxin in environmental or food samples. Currently, the conventional polymerase chain reaction (PCR) or immunoassay is the most broadly used assay to detect the toxin. However, these assays are laborious, time-consuming, and costly. More recently, numerous studies have described novel, highly sensitive, and portable methods for detecting Stxs from EHEC. To contextualize newly emerging Stxs detection methods, we briefly explain the basic principles of these methods, including lateral flow assays, optical detection, and electrical detection. We subsequently describe existing and newly emerging rapid detection technologies to identify and measure Stxs.

Keywords

Acknowledgement

This work was supported by the KRIBB Research Initiative Program (KGM5322321) and a grant from the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2018M3A9H4077992, 2022R1A2C1003699, 2022M3H9A1084279) and also the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (IITP-2022-RS-2022-00156212) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).

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