Bao Xiuli, Wang Siyuan, Hao Qingfang, Bai Yue, Li Siying, Zhang Shuai, Zhang Lei, Kang Xinxin, Lyu Mingsheng, Wang Shujun
Jiangsu Key Laboratory of Marine Bioresources and Environment/Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang 222005, China.
Co-Innovation Center of Jiangsu Marine Bio-Industry Technology, Jiangsu Ocean University, Lianyungang 222005, China.
Foods. 2024 Mar 20;13(6):941. doi: 10.3390/foods13060941.
Enrofloxacin (ENR) is widely used as a synthetic fluoroquinolone antibiotic for disease control in aquatic animals. ENR aptamers were screened in this study using the magnetic bead-SELEX method, and a graphene oxide fluorescent sensor was developed to detect the ENR residues in aquatic products. Firstly, ENR was conjugated to amino magnetic beads by amidation reaction, and then the aptamer sequences showing high affinity to ENR were screened step by step by using the SELEX screening method. Finally, after 10 rounds of SELEX screening, six candidate aptamers with high affinity were obtained. Among these, ENR-Apt 6 was selected based on its secondary structure features, high affinity ( = 35.08 nM), and high specificity to ENR. Furthermore, a fluorescent sensor was prepared using graphene oxide and ENR-Apt 6. The results showed that the linear range of the sensor could reach 600 nM (R = 0.986), while its optimal linear range was 1-400 nM (R = 0.991), with the lowest detection limit of 14.72 nM. The prepared sensor was successfully used for the detection of ENR in real samples, with a recovery range of 83.676-114.992% and a relative standard deviation < 10% for most of the samples.
恩诺沙星（ENR）作为一种合成氟喹诺酮类抗生素被广泛用于水生动物疾病防治。本研究采用磁珠-SELEX方法筛选ENR适配体，并开发了一种氧化石墨烯荧光传感器用于检测水产品中的ENR残留。首先，通过酰胺化反应将ENR偶联到氨基磁珠上，然后采用SELEX筛选方法逐步筛选出对ENR具有高亲和力的适配体序列。最后，经过10轮SELEX筛选，获得了6个具有高亲和力的候选适配体。其中，基于其二级结构特征、高亲和力（Kd = 35.08 nM）以及对ENR的高特异性，选择了ENR-Apt 6。此外，利用氧化石墨烯和ENR-Apt 6制备了一种荧光传感器。结果表明，该传感器的线性范围可达600 nM（R = 0.986），而其最佳线性范围为1 - 400 nM（R = 0.991），最低检测限为14.72 nM。所制备的传感器成功用于实际样品中ENR的检测，大多数样品的回收率范围为83.676 - 114.992%，相对标准偏差< 10%。
