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Meng X, Yu Y, Ma D, Mu M, Sun Q, Liu Q, Fan X, Li T, Chen J, Pan G, Zhou Z. Development of a colloidal gold immunochromatographic strip for the rapid on-site detection of Ecytonucleospora hepatopenaei (EHP). J Invertebr Pathol 2024; 209:108266. [PMID: 39701445 DOI: 10.1016/j.jip.2024.108266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 12/08/2024] [Accepted: 12/16/2024] [Indexed: 12/21/2024]
Abstract
The Pacific white shrimp (Penaeus vannamei), one of the world's most economically important aquatic species, is highly susceptible to Ecytonucleospora hepatopenaei (EHP), a pathogen that infects the hepatopancreas and causes hepatopancreatic microsporidiosis (HPM), leading to stunted growth and substantial economic losses in shrimp farming. Currently, no effective treatments for EHP exist, making rapid on-site detection and preventive measures essential for disease control. While nucleic acid-based detection methods are commonly employed, they require specialized equipment, controlled environments, and trained personnel, which increase costs. To address this limitation, we developed a colloidal gold immunochromatographic assay (GICA) strip for rapid on-site detection of EHP in shrimp farms. Using LC-MS/MS, 15 high-abundance EHP proteins were identified, with EhSWP3 ranked highest and selected as the optimal antigen detection target. Recombinant EhSWP3 was used to immunize mice, resulting in the development of monoclonal antibodies. The optimal capture and labeled antibody combination (1B6, 3A6) was identified and incorporated into the GICA strip. Testing with common shrimp pathogens and various microsporidia samples demonstrated the high specificity of the EHP test strip. The strip exhibited a sensitivity of 1.81 × 103 copies of the EHP-SSU rRNA gene for detecting EHP-infected shrimp and 1 × 104 purified EHP spores, indicating its strong sensitivity in practical applications. To facilitate on-site use, a simple GICA workflow was established using disposable pestles, Buffer A, and Buffer B, enabling detection within 15 min. Testing of 110 shrimp samples revealed a 90.0 % concordance between the GICA strip and qPCR results. This study marks the first development and application of an EHP antigen detection strip, offering a practical tool for rapid, on-site disease monitoring in shrimp farming.
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Affiliation(s)
- Xianzhi Meng
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China
| | - Yixiang Yu
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China
| | - Dandan Ma
- Chongqing Xinsaia Biotechnology Co., Ltd., No. 15, Ruihe Road, Chongqing 400799, PR China
| | - Mingxin Mu
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China
| | - Quan Sun
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China
| | - Quanlin Liu
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China
| | - Xiaodong Fan
- Key Laboratory of Conservation and Utilization of Pollinator Insect of the Upper Reaches of the Yangtze River (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Chongqing Normal University, No. 37 University City Road, Chongqing 400047, PR China
| | - Tian Li
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China
| | - Jie Chen
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China.
| | - Guoqing Pan
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China.
| | - Zeyang Zhou
- State Key Laboratory of Resource Insects, Chongqing Key Laboratory of Microsporidia Infection and Control, Southwest University, No. 2 Tiansheng Road, Chongqing 400715, PR China; Key Laboratory of Conservation and Utilization of Pollinator Insect of the Upper Reaches of the Yangtze River (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Chongqing Normal University, No. 37 University City Road, Chongqing 400047, PR China
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Zhao T, Shen Z, Zhong P, Zou H, Han M. Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:8210-8222. [PMID: 38175512 DOI: 10.1007/s11356-023-31612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024]
Abstract
The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, researchers mainly focus on the prediction of water quality parameters such as dissolved oxygen for early warning. To provide early warning directly from the pathogenic source, this study proposes an innovative approach for the detection and prediction of pathogenic microorganisms based on yellow croaker aquaculture. Specifically, a method based on quantitative polymerase chain reaction (qPCR) is designed to detect the Cryptocaryon irritans (Cri) pathogenic microorganisms. Furthermore, we design a predictive combination model for small samples and high noise data to achieve early warning. After performing wavelet analysis to denoise the data, two data augmentation strategies are used to expand the dataset and then combined with the BP neural network (BPNN) to build the fusion prediction model. To ensure the stability of the detection method, we conduct repeatability and sensitivity tests on the designed qPCR detection technique. To verify the validity of the model, we compare the combined BPNN to long short-term memory (LSTM). The experimental results show that the qPCR method provides accurate quantitative measurement of Cri pathogenic microorganisms, and the combined model achieves a good level. The prediction model demonstrates higher accuracy in predicting Cri pathogenic microorganisms compared to the LSTM method, with evaluation indicators including mean absolute error (MAE), recall rate, and accuracy rate. Especially, the accuracy of early warning is increased by 54.02%.
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Affiliation(s)
- Tong Zhao
- College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
| | - Zhencai Shen
- College of Science, China Agricultural University, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China
| | - Ping Zhong
- College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, China
- College of Science, China Agricultural University, Beijing, 100083, China
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China
| | - Hui Zou
- College of Science, China Agricultural University, Beijing, 100083, China.
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, 100083, China.
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China.
| | - Mingming Han
- Zhejiang Academy of Agricultural Sciences, Zhejiang, 310021, China
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Chen H, Zhou T, Li S, Feng J, Li W, Li L, Zhou X, Wang M, Li F, Zhao X, Ren L. Living Magnetotactic Microrobots Based on Bacteria with a Surface-Displayed CRISPR/Cas12a System for Penaeus Viruses Detection. ACS APPLIED MATERIALS & INTERFACES 2023; 15:47930-47938. [PMID: 37811735 DOI: 10.1021/acsami.3c09690] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Bacterial microrobots are an emerging living material in the field of diagnostics. However, it is an important challenge to make bacterial microrobots with both controlled motility and specific functions. Herein, magnetically driven diagnostic bacterial microrobots are prepared by standardized and modular synthetic biology methods. To ensure mobility, the Mms6 protein is displayed on the surface of bacteria and is exploited for magnetic biomineralization. This gives the bacterial microrobot the ability to cruise flexibly and rapidly with a magnetization intensity up to about 18.65 emu g-1. To achieve the diagnostic function, the Cas12a protein is displayed on the bacterial surface and is used for aquatic pathogen nucleic acid detection. This allows the bacterial microrobot to achieve sensitive, rapid, and accurate on-site nucleic acid detection, with detection limits of 8 copies μL-1 for decapod iridescent virus 1 (DIV1) and 7 copies μL-1 for white spot syndrome virus (WSSV). In particular, the diagnostic results based on the bacterial microrobots remained consistent with the gold standard test results when tested on shrimp tissue. This approach is a flexible and customizable strategy for building bacterial microrobots, providing a reliable and versatile solution for the design of bacterial microrobots.
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Affiliation(s)
- Haoxiang Chen
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Tao Zhou
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Shuo Li
- College of Life Science and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China
| | - Junya Feng
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Wenlong Li
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Lihuang Li
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Xi Zhou
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Miao Wang
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
| | - Fang Li
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, P. R. China
| | - Xueqin Zhao
- College of Life Science and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou 310018, P. R. China
| | - Lei Ren
- Department of Biomaterials, The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, P. R. China
- State Key Lab of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, P. R. China
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