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All Journal Indonesian Journal of Biotechnology
Nurmalasari Darsono
Biology Department, Faculty of Science and Technology, Airlangga University, Jalan Mulyorejo, Surabaya 60115, Indonesia; Research Center for Genetic Engineering, National Research and Innovation Agency, BJ Habibie Building, Jalan MH Thamrin 8, Jakart
Biochemistry, Genetics & Molecular Biology Immunology & microbiology Materials Science & Nanotechnology

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The efficacy of a chicken antibody for the development of immunoassay‐based rapid detection in sugarcane mosaic virus disease Nurmalasari Darsono; Widhi Dyah Sawitri; Retnosari Apriasti; Agus Heri Setyo Wahyudi; Putri Andreyna Saragi; Victorin Mega Putri; Sugiharto Sugiharto; Win Darmanto
Indonesian Journal of Biotechnology Vol 28, No 1 (2023)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijbiotech.74104

Abstract

Sugarcane Mosaic Virus (SCMV) infection is one of the most serious problems that can result in severe yield loss of sugarcane. Since the symptoms of SCMV infection are similar to other biotic and abiotic stress symptoms, the development of a rapid diagnostic with high precision is required. The use of laboratory animals such as rabbits is required for antibody production in immunoassay‐based detection. However, due to its many advantages, specific chicken egg yolk immunoglobulin (IgY) has received considerable attention as an alternative antibody production in immunodiagnostics for infectious diseases. In this study, IgY antibody against SCMV recombinant coat protein (CP) was successfully obtained from chicken blood serum and tested to compare its efficacy against antibody from rabbit (IgG) using immunocapture reverse transcription‐polymerase chain reaction (IC‐RT‐PCR). The result showed that IgY and IgG could detect 0.1 g SCMV infected leaves using 1000‐times‐diluted antibodies. The IgY antibody was also confirmed to be reproducible and potentially applicable in plant disease diagnostics using an antibody‐based detection.
Co-Authors Agus Heri Setyo Wahyudi Putri Andreyna Saragi Retnosari Apriasti Sugiharto Victorin Mega Putri Widhi Dyah Sawitri Win Darmanto