A Quantum Dot Nanobiosensor for Rapid Detection of Botulinum Neurotoxin Serotype E.
Abstract
Botulinum neurotoxins (BoNTs) are potent toxins produced by Clostridium bacteria that are responsible for the illness botulism and are listed as bioterrorism agents. BoNT serotype E (BoNT/E) is one of four BoNT serotypes that cause human botulism and is the second most frequent cause of foodborne botulism. Rapid detection and discrimination of BoNT serotypes implicated in human disease is critical for ensuring timely treatment of patients and identifying sources of toxins, but there have been few reported detection methods for BoNT/E, and even fewer methods usable for BoNT serotyping. We report a nanobiosensor based on Förster Resonance Energy Transfer (FRET) between semiconductor nanocrystals (quantum dots, QDs) and dark quencher-labeled peptide probes to detect biologically-active BoNT/E in aqueous media. The peptide probes contain a specific cleavage site for active BoNT/E. QD photoluminescence (PL), which changes intensity due to FRET when the peptide probe is cleaved, was used to indicate toxin presence and quantity. The detection of BoNT/E light chain (LcE) and holotoxin was observed within 3 h. The limits of detection were 0.02 ng/mL and 2 ng/mL for LcE and holotoxin, respectively. The nanobiosensor shows good specificity toward the target in tests with non-target BoNT serotypes. The high sensitivity, simple operation, short detection time, and ability to use in parallel with probes developed for other BoNT serotypes indicates that the nanobiosensor will be useful for…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.