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ORIGINAL RESEARCH article
Front. Microbiol.
Sec. Virology
Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1533170
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Background: Areca Palm Velarivirus 1 (APV1), the causal agent of betel palm yellow leaf disease (YLD), has caused significant yield losses and decreased product quality of betel nuts, posing a serious economic threat to local farmers. There is an urgent need for a convenient and reliable method for the rapid detection and surveillance of APV1.The Capsid protein (CP) of APV1 was expressed in Escherichia coli and purified as antigen to immunize BALB/c mice. Two specific monoclonal antibodies monoclonal antibodies ((MAbs)), APV1CP-1 and APV1CP-10, were generated through the hybridoma technique. APV1CP-1 was conjugated with colloidal gold served as detection reagent, while APV1CP-10 was coated onto a porous nitrocellulose membrane to act as the detection line. Goat anti-mouse IgG was used as the control line.These components were then assembled into a colloidal gold immunochromatographic strip (CGICS) for effective detection of APV1.The MAbs monoclonal antibodies APV1CP-1 and APV1CP-10 were successfully obtained with titers exceeding 1:102,400. Colloidal gold particles used in the assay had an approximate diameter of 30-40 nm, and exhibited a surface plasmon resonance peak around 530 nm. The CGICS allowed for the detection of APV1 by applying infected sap to the test strip, with results visible within 5-10 minutes. The test showed no cross-reactivity with other viruses tested, and the visual detection limit for APV1 was established at a 100-fold dilutions of APV1-infected leaf samples.: The monoclonal antibody-based colloidal gold immunochromatographic strip developed in this study demonstrates significant convenience, rapidity, and 设置了格式: 字体: 倾斜 设置了格式: 字体: 小四 reliability for APV1 detection. These advancements are anticipated to facilitate rapid diagnosis and surveillance of APV1 in field settings.
Keywords: areca palm velarivirus 1 (APV1), Monoclonal antibodiesMAbs, Colloidal Gold Immunochromatographic strip, Rapid detection, surveillance
Received: 23 Nov 2024; Accepted: 10 Feb 2025.
Copyright: © 2025 Cao, Lu, He, Liu, Li, Huang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xianmei Cao, Hainan University, Haikou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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