Enzyme Cascade Amplification-Based Immunoassay Using Alkaline Phosphatase-Linked Single-Chain Variable Fragment Fusion Tracer and MnO2 Nanosheets for Detection of Deoxynivalenol in Corn Samples

Abstract

Deoxynivalenol (DON) is a common mycotoxin that contaminates cereals. Therefore, the development of sensitive and efficient detection methods for DON is essential to guarantee food safety and human health. In this study, an enzyme cascade amplification-based immunoassay (ECAIA) using a dual-functional alkaline phosphatase-linked single-chain fragment variable fusion tracer (scFv-ALP) and MnO₂ nanosheets was established for DON detection. The scFv-ALP effectively catalyzes the hydrolysis of ascorbyl-2-phosphate (AAP) to produce ascorbic acid (AA). This AA subsequently interacts with MnO₂ nanosheets to initiate a redox reaction that results in the loss of oxidizing properties of MnO₂. In the absence of ALP, MnO₂ nanosheets can oxidize 3,3',5,5'-tetramethylbenzidine (TMB) to produce the blue oxidized product of TMB, which exhibits a signal at a wavelength of 650 nm for quantitative analysis. After optimization, the ECAIA had a limit of detection of 0.45 ng/mL and a linear range of 1.2-35.41 ng/mL. The ECAIA exhibited good accuracy in recovery experiments and high selectivity for DON. Moreover, the detection results of the actual corn samples correlated well with those from high-performance liquid chromatography. Overall, the proposed ECAIA based on the scFv-ALP and MnO₂ nanosheets was demonstrated as a reliable tool for the detection of DON in corn samples.

Keywords: MnO2 nanosheets; alkaline phosphatase; cereal; immunoassay; mycotoxin; single-chain fragment variable.

Figure 1

Characterization of fusion protein scFv-ALP. (A) SDS-PAGE analysis of the purified scFv-ALP. Lane M: prestained protein marker; lane 1: purified scFv-ALP with a molecular weight of 81 kDa. (B) Analysis of the enzymatic activity and antibody functionality within scFv-ALP. Inset: diagrams of the reaction wells of the 96-well microplate. (C) Analysis of the detection performance of scFv-ALP by the direct competitive ELISA. Error bars denote the standard deviations (SDs) of three independent experiments.

Figure 2

Characterization of MnO₂ nanosheets. (A) TEM analysis. (B) AFM analysis, inset: the spectrum of nanosheet thickness analysis, and (C) Zeta potential test.

Scheme 1

Illustration of ECAIA based on scFv-ALP and nanozyme sensing system.

Figure 3

The UV–visible absorption spectra of different components in the MnO₂-TMB system are shown. (a) scFv-ALP; (b) MnO₂ nanosheets; (c) substrate solution AB containing TMB; (d) AAP+MnO₂ nanosheets; (e) Mn²⁺+substrate solution AB containing TMB; (f) MnO₂ nanosheets+substrate solution AB containing TMB; (g) MnO₂ nanosheets+AA+substrate solution AB containing TMB; (h) MnO₂ nanosheets+AAP+substrate solution AB containing TMB; (i) scFv-ALP+MnO₂ nanosheets+AAP+ substrate solution AB containing TMB. Illustrations corresponding to different reaction components are presented. The components included 1 mM of MnO₂ nanosheets, 1 mM of MnCl₂, 1 mM of AAP, 1 mM of AA, 10 μL of 10 μg/mL scFv-ALP, 50 μL of TMB solution, 40 mM of Tris-HCl (pH 10), and 40 mM of NaAc-HAc (pH 3.8).

Figure 4

Development of the ECAIA for DON. Optimization of DON-BSA concentration (A), ionic strength (B), pH (C), MeOH concentration (D), and competitive time (E) for ECAIA. (F) The standard competitive inhibition curve of ECAIA. The error bar represents the standard deviation from three independent experiments.