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Journal Article

Citation

Stern D, Pauly D, Zydek M, Müller C, Avondet MA, Worbs S, Lisdat F, Dorner MB, Dorner BG. Biosens. Bioelectron. 2015; 78: 111-117.

Affiliation

Biological Toxins (ZBS 3), Centre for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany. Electronic address: DornerB@rki.de.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.bios.2015.11.020

PMID

26599480

Abstract

Ricin is one of the most toxic plant toxins known. Its accessibility and relative ease of preparation makes it a potential agent for criminal or bio-terrorist attacks. Detection of ricin from unknown samples requires differentiation of ricin from the highly homologous Ricinus communis agglutinin which is currently not feasible using immunological methods. Here we have developed a simple and sensitive surface plasmon resonance (SPR) sensing system for rapid differentiation between ricin and agglutinin done in real time. Both lectins were quantified in a sandwich immunoassay-like setting by capturing with a cross-reactive antibody (R109) binding to both proteins while differentiating by injection of a ricin-specific antibody (R18) in a subsequent enhancement step. The SPR-assay was reproducible and sensitive for different R. communis cultivars, showing no false positive results when other lectins were tested. Quantification and differentiation of both molecules was also demonstrated from a crude castor bean extract and complex matrices. For the first time, we have demonstrated how the closely related lectins can be discerned and quantified in a single assay based on immunological methods. This novel approach delivers crucial information regarding the composition, purity, concentration, and toxicity of suspicious samples containing ricin in less than 30 minutes. Furthermore, we show how enhancement injections during SPR-measurements can be used to determine the ratio of two related proteins independently of the actual protein concentration by comparing normalized enhancement response levels.


Language: en

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