Novel Label-Free GelRed-Aptamer Sensor for the Rapid Determination of Deoxynivalenol in Wheat Caused by Fusarium graminearum

Abstract

Deoxynivalenol (DON) is the key virulence factor in Fusarium head blight. However, rapid and low-cost methods for DON are limited. Herein, a novel label-free GelRed-aptamer sensor was developed for the detection of DON. GelRed was embedded in double strain DNA (dsDNA) consisting of a single chain aptamer and its complementary DNA (cDNA), resulting in the increase of fluorescence intensity. However, DON preferentially combined with the aptamer and did not combine with its cDNA, which would result in the decrease in the formation of the GelRed/aptamer/cDNA complex, greatly reducing the fluorescence intensity. The samples were extracted from wheat, centrifuged, and filtered through a 0.22 μm membrane. Good linearity was obtained for DON from 3.13 to 313 mg L⁻¹. The limit of quantitation (LOQ) for wheat samples was 6.07 mg L⁻¹, providing a low cost method for DON compared with enzyme-linked immunosorbent assays. Experiments with spiked samples showed good recoveries from 92% to 105% with relative standard deviations less than 5.33% with analysis within 20 min. This method is simple and low cost with potential applications in agriculture and food.

Keywords:

• Deoxynivalenol
• fluorescence intensity
• Fusarium graminearum
• GelRed-aptamer sensor
• Fusarium head blight

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Mei Wu. The first draft of the manuscript was written by Mei Wu and Yan-Zhen Mei. Zhi Yang and Zhi-Chun Zhan provided support for wheat samples and practical applications. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Disclosure statement

The authors declare that they have no conflicts of interest.

Additional information

Funding

This study was supported by grants from National Natural Science Foundation of China (grant number: 32070058) and Key Technologies Research and Development Program of China (2018YFC1604206).