Development of a triple antibody sandwich enzyme-linked immunosorbent assay for cassava mosaic disease detection using a monoclonal antibody to Sri Lankan cassava mosaic virus

Author:

Charoenvilaisiri SaengsoonORCID,Seepiban Channarong,Kumpoosiri Mallika,Rukpratanporn Sombat,Warin Nuchnard,Phuangrat Bencharong,Chitchuea Phakamat,Siripaitoon Sirima,Chatchawankanphanich Orawan,Gajanandana Oraprapai

Abstract

Abstract Background Cassava mosaic disease (CMD) is one of the most devastating viral diseases for cassava production in Africa and Asia. Accurate yet affordable diagnostics are one of the fundamental tools supporting successful CMD management, especially in developing countries. This study aimed to develop an antibody-based immunoassay for the detection of Sri Lankan cassava mosaic virus (SLCMV), the only cassava mosaic begomovirus currently causing CMD outbreaks in Southeast Asia (SEA). Methods Monoclonal antibodies (MAbs) against the recombinant coat protein of SLCMV were generated using hybridoma technology. MAbs were characterized and used to develop a triple antibody sandwich enzyme-linked immunosorbent assay (TAS-ELISA) for SLCMV detection in cassava leaves and stems. Assay specificity, sensitivity and efficiency for SLCMV detection was investigated and compared to those of a commercial ELISA test kit and PCR, the gold standard. Results A TAS-ELISA for SLCMV detection was successfully developed using the newly established MAb 29B3 and an in-house polyclonal antibody (PAb) against begomoviruses, PAb PK. The assay was able to detect SLCMV in leaves, green bark from cassava stem tips, and young leaf sprouts from stem cuttings of SLCMV-infected cassava plants without cross-reactivity to those derived from healthy cassava controls. Sensitivity comparison using serial dilutions of SLCMV-infected cassava sap extracts revealed that the assay was 256-fold more sensitive than a commercial TAS-ELISA kit and 64-fold less sensitive than PCR using previously published SLCMV-specific primers. In terms of DNA content, our assay demonstrated a limit of detection of 2.21 to 4.08 × 106 virus copies as determined by quantitative real-time PCR (qPCR). When applied to field samples (n = 490), the TAS-ELISA showed high accuracy (99.6%), specificity (100%), and sensitivity (98.2%) relative to the results obtained by the reference PCR. SLCMV infecting chaya (Cnidoscolus aconitifolius) and coral plant (Jatropha multifida) was also reported for the first time in SEA. Conclusions Our findings suggest that the TAS-ELISA for SLCMV detection developed in this study can serve as an attractive tool for efficient, inexpensive and high-throughput detection of SLCMV and can be applied to CMD screening of cassava stem cuttings, large-scale surveillance, and screening for resistance.

Funder

National Science and Technology Development Agency

Publisher

Springer Science and Business Media LLC

Subject

Infectious Diseases,Virology

Reference36 articles.

1. Food and Agriculture Organization of the United Nations. FAOSTAT statistics database, FAO (2018). http://www.fao.org/faostat/en/#data/QC. Accessed 22 Nov 2020.

2. Sarker MNI, Hossin MA, Yin X, Sarkar MK. One belt one road initiative of China: implication for future of global development. Mod Econ. 2018;9:623–38.

3. Thai Tapioca Starch Association (TTSA). Export tapioca products (2019). http://www.thaitapiocastarch.org/en/information/statistics/export_tapioca_products. Accessed 22 Nov 2020.

4. Patil BL, Fauquet CM. Cassava mosaic geminiviruses: actual knowledge and perspectives. Mol Plant Pathol. 2009;10:685–701.

5. Rybicki EP. A top ten list for economically important plant viruses. Arch Virol. 2015;160:17–20.

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