Journal of Applied Biotechnology Reports

Journal of Applied Biotechnology Reports

Polyclonal Antibody-Based Sandwich ELISA for Detection of Escherichia coli O157:H7

Document Type : Original Article

Authors
1 Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
2 Department of Biology, Faculty of Basic Sciences, Imam Hossein University, Tehran, Iran
3 Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
Abstract
Introduction: Shiga toxin-producing E. coli (STEC) are bacteria causing severe foodborne diseases, with E. coli O157:H7 being a significant public health concern. Infection may occur following exposure to or drinking the contaminated water or the consumption of the contaminated food, especially meat and dairy products. We aimed to optimize a sandwich ELISA method using purified poly-IgG against HI chimera protein for E. coli O157:H7 detection.
Materials and Methods: We induced the recombinant chimeric antigen (HI) in a prokaryotic host and purified it through a Ni-NTA column. After refolding the antigen, mice and rabbits were immunized and the poly-IgGs were purified from sera using a protein G column.
Results: Recombinant HI (60 kDa) was expressed in E. coli BL21 (yield: 1.2 mg/L) and purified via the Ni-NTA column. Antibodies were generated in mice and rabbits, serving as detection and capture antibodies. The optimized antibody concentrations were 1.25 μg/ml for capture and 0.312 μg/ml for detection. Our sandwich ELISA demonstrated high sensitivity (limit of detection: 104 CFU/ml) and specificity for E. coli O157:H7, confirmed by testing against different bacteria.
Conclusions: Our developed sandwich ELISA has proven to be a highly sensitive method for the detection of E. coli O157:H7, capable of reliably detecting bacterial concentrations as low as 104 CFU/ml. 
Keywords

Volume 11, Issue 2
Spring 2024
Pages 1313-1321

  • Receive Date 04 September 2023
  • Revise Date 25 November 2023
  • Accept Date 20 December 2023