Elsevier

Food Chemistry

Volume 361, 1 November 2021, 129901
Food Chemistry

A sensitive immunosensor based on graphene-PAMAM composites for rapid detection of the CP4-EPSPS protein in genetically modified crops

https://doi.org/10.1016/j.foodchem.2021.129901Get rights and content

Highlights

  • A facile electrochemical sensing platform was constructed.

  • The immunosensor could amplify the signal by gold nanoparticles-labeled antibody.

  • The sensor was used to monitor CP4-EPSPS protein in GM crops.

  • The sensor was with high specificity, reproducibility and accuracy.

  • The sensor could detect soybean RRS and maize NK603 at contents of 0.01% and 0.03%.

Abstract

A simple electrochemical immunosensor based on nitrogen-doped graphene and polyamide-amine (GN-PAM) composites was proposed for the detection of the CP4-EPSPS protein in genetically modified (GM) crops. In this immunosensor, the amplification of the detection signal was realized through antibodies labeled with gold nanoparticles (AuNPs). The electrochemical responses of the immunosensor were linear (R2 = 0.9935 and 0.9912) when the GM soybean RRS and maize NK603 content ranged from 0.025% to 1.0% and 0.05% to 1.5%, respectively. The limits of detection for the GM soybean RRS and maize NK603 were as low as 0.01% and 0.03%, respectively. The immunosensor also exhibited high specificity, and satisfactory stability, reproducibility, and accuracy. Our findings indicated that the constructed immunosensor provides a new approach for the sensitive detection of the CP4-EPSPS protein. Notably, the sensor may be applied to other proteins or pathogenic bacteria by simply changing the antibodies, and may also be used for multi-component analysis.

Introduction

Genetically modified (GM) technology has rapidly developed in recent decades, increasing the types and planting acreage of GM crops by more than 100 times (from 1.7 million hectares in 1996 to 197 million hectares in 2018) because of the huge application potential in agriculture and other fields (Gao, Wen, Wu, Fu, & Wu, 2017). As the most representative type of GM crop, herbicide-resistant crops are widely planted around the world. By transferring the exogenous CP4-epsps gene from Agrobacterium tumefaciens into the genome to express the CP4-EPSPS protein, the crops are conferred glyphosate tolerance (Mathur et al., 2017, Lin and Pan, 2016). The food and environmental safety of GM crops and their products is still the focus of much attention. Thus far, only GM cotton and papaya have been approved for commercial planting in China. However, the illegal cultivation and sale of GM crops still occur occasionally, and GM ingredients have been repeatedly detected in rice products exported from China. Many countries or areas also implement mandatory labeling measures for GM products (Shuang, Wang, Zhu, Zhou, & Wu, 2017). Therefore, it is urgent to strengthen the safe supervision of illegal production and sale of GM products.

PCR-based techniques with high sensitivity are the most widely used methods for GM crop detection (Eum, Kim, Lim, Lee, & Choi, 2019). In particular, real-time quantitative PCR is considered the standard method for quantitative analysis of GM crops (Wang et al., 2018). However, the need for precision instruments, professional technicians, and tedious steps is a notable limitation. Immunological-based methods, such as ELISA and immunoassay test strips, are commonly used to detect GM crops at the protein level (Evangelista et al., 2015, Liu et al., 2020). The advantages of these methods include simple operation, visualization of results and low cost. Thus, they are popular for the detection GM proteins in specific locations. Unfortunately, these protein-based methods only provide qualitative or semi-quantitative results and insufficient sensitivity.

Electrochemical immunosensors could convert the antigen–antibody binding signal into an electrochemical signal. As a result of the signal amplification effect of nanomaterials, such as mesoporous carbon (Wang, Hu, et al., 2019); carbon nanotubes (Shen et al., 2019); graphene (Khetani et al., 2018); metal nanoparticles (NPs), e.g., gold (Au) (Bhardwaj, Pandey, Rajesh, & Sumana, 2019), platinum (Pt) (Yang et al., 2016), palladium (Pd) (Li et al., 2018), and alloy NPs (Li et al., 2018); and recognition elements (e.g., enzymes, receptors or antibodies), immunosensor has the advantages of strong specificity, high sensitivity, good accuracy, and wide application. Thus, it is highly suitable for sensitive detection where precise molecular methods are unavailable. Immunosensors have been widely used for the analysis of foodborne pathogens (Wang, Xiu, et al., 2019), mycotoxins (Jiang et al., 2019), heavy metal ions (Miao, Tang, & Wang, 2017), pesticide and antibiotics residues (Shivani and Kumar, 2018, Stevenson et al., 2019) and prohibited additives (Zhang et al., 2016).

In this study, a simple electrochemical immunosensor based on nitrogen-doped graphene (GN) and polyamide-amine (PAMAM) composites was proposed for the detection of the CP4-EPSPS protein in GM crops. The GN-PAM-AuNPs-based sensor was designed as illustrated in Scheme 1 based on three main points: (1) GN primarily offered an effective platform with a highly specific surface area and conductivity, and promoted antibody assembly after being coupled with PAMAM. (2) The dendrimer PAMAM, with numerous amino groups as a functional molecule, was used to immobilize antibodies. (3) Gold nanoparticles (AuNPs) were used to enhance electrochemical signals and increase the specific adsorption of antibodies. The constructed electrochemical sensing platform exhibited high sensitivity, specificity, and reproducibility with short analysis time and portability. Furthermore, it could serve as a useful tool for analyzing the CP4-EPSPS protein in GM crops.

Section snippets

Materials and equipment

The monoclonal antibody (mAb) against the CP4-EPSPS protein was purchased from Ronghui (Shanghai, China). Colloidal gold was obtained by trisodium citrate reduction of chloroauric acid, and the particle size was about 40 nm. The dendrimer PAMAM methanol solution (Generation 0, 20%) was purchased from Macklin (Shanghai, China). Nitrogen-doped graphene was purchased from XFNANO (Nanjing, China). Bovine serum albumin (BSA) was purchased from Sigma (St. Louis, MO, USA). Seed powder standards of the

Characterization of GN-PAM composites

GN-PAM composites were prepared using a one-step ultrasonic method, which adsorbed PAMAM onto the GN nanomaterial. SEM images were utilized to characterize the micromorphology of the GN-PAM composites coated on the GCE. As shown in Fig. 1A, GN-PAM composites had an increased loading capacity to form a uniform film structure on GCE. The SEM image of the GN-modified electrode is shown in Fig. 1B. Many voids on the surface of the GCE can be observed (red arrows).

Optimization of AuNPs labeling mAb conditions

Coupling AuNPs with mAb was

Discussion and conclusions

Different methods for the determination of GM crops are shown in Table 2. PCR-based methods (shown in No. 1–4) were based on finding the inserted foreign gene and required a longer sample processing time. Moreover, the PCR-based methods failed to detect the expression level of exogenous proteins in GM crops (Rosa et al., 2016). ELISA and the test strip (shown in No. 5 and 6) were less sensitive compared with PCR-based methods (Dong et al., 2016, Zeng et al., 2020). Immunosensors focused on the

CRediT authorship contribution statement

Haijuan Zeng: Data curation, Formal analysis, Writing - original draft. Qianwen Yang: Data curation, Formal analysis. Hua Liu: . Guogan Wu: . Wei Jiang: . Xiaofeng Liu: . Jinbin Wang: Writing - review & editing. Xueming Tang: .

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The research was supported by the Shanghai Sailing Program No. 20YF1443000, Shanghai Natural Science Foundation No. 19ZR1436800, Technology Support Project No. KJZC202008, Shanghai Fresh Corn Technology System Project No.10 2017, SAAS Project on Agricultural Science and Technology Innovation Supporting Area [SAAS Application Base Study 2021 (09), SAAS Platform 2021 (08)] and the SAAS Program for Excellent Research Team No. 2017 (B-07).

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