ArticleScientific reports2024
Computational design and evaluation of a polyvalent vaccine for viral nervous necrosis (VNN) in fish to combat Betanodavirus infection.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The multifaceted challenge of viral nervous necrosis in fish farming: a review of current knowledge and control.Veterinary research communications · 2026Review
- Immunoinformatic strategy for developing multi-epitope subunit vaccine against Helicobacter pylori.PloS one · 2025Article
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Authors and funding
16 authors.
Funding
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Abstract
Viral nervous necrosis (VNN) poses a significant threat to the aquaculture industry, causing substantial losses and economic burdens. The disease, attributed to nervous necrosis viruses within the Betanodavirus genus, is particularly pervasive in the Mediterranean region, affecting various fish species across all production stages with mortality rates reaching 100%. Developing effective preventive measures against VNN is imperative. In this study, we employed rigorous immunoinformatics techniques to design a novel multi-epitope vaccine targeting VNN. Five RNA-directed RNA polymerases, crucial to the lifecycle of Betanodavirus, were selected as vaccine targets. The antigenicity and favorable physicochemical properties of these proteins were confirmed, and epitope mapping identified cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte epitopes essential for eliciting a robust immune response. The selected epitopes, characterized by high antigenicity, non-allergenicity, and non-toxicity, were further enhanced by adding PADRE sequences and hBD adjuvants to increase immunogenicity. Two vaccine constructs were developed by linking epitopes using appropriate linkers, demonstrating high antigenicity, solubility, and stability. Molecular dynamics simulations revealed stable interactions between the vaccine constructs and Toll-like receptors (TLRs), essential for pathogen recognition and immune response activation in fish. Notably, vaccine construct V2 exhibited superior stability and binding affinity with TLR8, suggesting its potential as a promising candidate for VNN prevention. Overall, our study presents a comprehensive approach to VNN vaccine design utilizing immunoinformatics, offering safe, immunogenic, and effective solutions across multiple Betanodavirus species. Further experimental validation in model animals is recommended to fully assess the vaccine's efficacy. This research contributes to improved vaccine development against diverse fish pathogens by addressing emerging challenges and individualized immunization requirements in aquaculture.
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