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Published on: August 12, 2021
Streamlined detection of Nipah virus antibodies using a split NanoLuc biosensor
Éric Bergeron1,2, Cheng-Feng Chiang1, Michael K Lo1
1Viral Special Pathogens Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, USA.
A new Nipah virus (NiV) antibody test using a split NanoLuc luciferase biosensor offers a simple, fast, and reliable method for NiV surveillance. This assay demonstrates high sensitivity and specificity, aiding in tracking NiV infections and understanding transmission dynamics.
Area of Science:
- Virology
- Immunology
- Biotechnology
Background:
- Nipah virus (NiV) is an emerging zoonotic RNA virus causing severe respiratory and neurological diseases in humans and animals.
- Accurate diagnostics and surveillance are essential for managing NiV outbreaks, understanding transmission, and identifying infections.
Purpose of the Study:
- To develop and validate a novel split NanoLuc luciferase Nipah virus glycoprotein (G) biosensor for detecting anti-NiV antibodies.
- To assess the usability of this biosensor for clinical and animal sample analysis.
Main Methods:
- Development of a split NanoLuc luciferase NiV glycoprotein (G) biosensor.
- Validation using the WHO's first international standard for anti-NiV antibodies and over 700 serum samples from Bangladesh.
- Comparison with NiV neutralization assays and anti-NiV IgG ELISA.
Main Results:
- The anti-NiV-G biosensor assay demonstrated high sensitivity (98.6%) and specificity (100%), comparable to ELISA.
- Antibodies from NiV survivors persisted for at least 8 years.
- The assay could not detect antibodies in samples collected less than a week post-symptom onset.
Conclusions:
- The developed anti-NiV-G biosensor is a simple, fast, and reliable tool for NiV antibody detection.
- This biosensor can enhance NiV surveillance and aid in retrospective outbreak investigations.
- Further development may be needed to improve early detection capabilities.
