Production of antigens expressed in Nicotiana benthamiana plant and Escherichia coli for the SARS-CoV-2 IgG antibody detection by ELISA
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Summary
This summary is machine-generated.Plant-based expression systems can produce SARS-CoV-2 antigens for diagnostic tests, showing high sensitivity and specificity. This offers a viable alternative for developing crucial COVID-19 diagnostic kits, especially in resource-limited settings.
Area Of Science
- Biotechnology
- Virology
- Immunology
Background
- The COVID-19 pandemic highlighted global shortages of diagnostic kits.
- Heterologous protein expression systems are crucial for rapid antigen production.
- Plant-based expression systems offer a potential alternative for recombinant antigen development.
Purpose Of The Study
- To evaluate novel SARS-CoV-2 antigens produced in Nicotiana benthamiana using a transient expression system.
- To compare the diagnostic performance of plant-expressed antigens with those produced in Escherichia coli.
- To assess the sensitivity and specificity of diagnostic tests utilizing these different antigens.
Main Methods
- Transient expression of SARS-CoV-2 S1-N and nucleocapsid N proteins in Nicotiana benthamiana.
- Expression of two distinct SARS-CoV-2 antigens (QCoV9 and QCoV7) in Escherichia coli.
- Enzyme-Linked Immunosorbent Assays (ELISAs) to detect anti-SARS-CoV-2 IgG antibodies using four different antigens.
- Evaluation of test sensitivity using 816 samples from COVID-19 patients and specificity using 195 pre-pandemic samples.
Main Results
- Consistent test sensitivity ranging from 85% to 88% was observed across all evaluated SARS-CoV-2 antigens.
- High specificity of 97.5% was achieved, irrespective of the antigen or expression system used.
- The expression system (plant vs. E. coli) did not significantly impact diagnostic performance.
Conclusions
- Plant expression systems, specifically Nicotiana benthamiana, are effective platforms for producing SARS-CoV-2 recombinant antigens.
- Diagnostic tests utilizing these plant-expressed antigens demonstrate reliable sensitivity and specificity.
- This approach provides a promising alternative for developing accessible diagnostic tools, particularly in resource-constrained environments.
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