Evaluation of the immunogenic properties of a chimeric protein against West Nile virus: in-silico analysis and assessment in mouse
-
Mahdi Arezoumandi
,
Sadegh Shojaei Baghini
,
Nima Rad
,
Elham Taghipour
,
Mohammad Hassan Pouriayevali
,
Fateme Frootan
,
Mostafa Motallebi
,
Ali Hatef Salmanian
Abstract
Background and Objectives: West Nile virus (WNV) is a mosquito-borne flavivirus that represents a significant public health concern, and the development of effective vaccine candidates remains a priority. This study aimed to design a chimeric multi-epitope protein against WNV using immunoinformatics approaches and evaluate its immunogenicity in mice.
Materials and Methods: Selected regions of the WNV envelope (E) and non-structural 1 (NS1) proteins were analyzed to identify B- and T-cell epitopes using immunoinformatics tools. The selected epitopes were incorporated into a chimeric construct fused with cholera toxin B subunit, and its antigenicity, allergenicity, structural properties, and TLR3 interaction were evaluated in silico. The optimized construct was expressed in Escherichia coli, purified by Ni-NTA affinity chromatography, and confirmed by SDS-PAGE and western blotting. BALB/c mice were immunized with the recombinant protein plus adjuvant, and immune responses were assessed using ELISA, cytokine assays, and flow cytometry.
Results: Immunized mice developed high IgG antibody titers and showed detectable IFN-γ and IL-5 production, suggesting induction of Th1-associated responses with limited Th2-related cytokine production. The chimeric protein also stimulated CD4+ and CD8+ T-cell responses, indicating activation of both humoral and cellular immunity.
Conclusion: The designed chimeric protein showed immunogenic potential and induced humoral and cellular immune responses in mice, supporting further evaluation as a candidate for WNV vaccine development.
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31. Salvador EA, Pires de Souza GA, Cotta Malaquias LC, Wang T, Leomil Coelho LF. Identification of relevant regions on structural and nonstructural proteins of Zika virus for vaccine and diagnostic test development: an in silico approach. New Microbes New Infect 2019; 29: 100506.
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34. Shrestha B, Wang T, Samuel MA, Whitby K, Craft J, Fikrig E, et al. Gamma interferon plays a crucial early antiviral role in protection against West Nile virus infection. J Virol 2006; 80: 5338-5348.
35. Acharya D, Wang P, Paul AM, Dai J, Gate D, Lowery JE, et al. Interleukin-17A promotes CD8+ T cell cytotoxicity to facilitate West Nile virus clearance. J Virol 2017; 91(1): e01529-16.
36. Schneeweiss A, Chabierski S, Salomo M, Delaroque N, Al-Robaiy S, Grunwald T, et al. A DNA vaccine encoding the E protein of West Nile virus is protective and can be boosted by recombinant domain DIII. Vaccine 2011; 29: 6352-6357.
37. Nelson MH, Winkelmann E, Ma Y, Xia J, Mason PW, Bourne N, et al. Immunogenicity of RepliVAX WN, a novel single-cycle West Nile virus vaccine. Vaccine 2010; 29: 174-182.
2. Slon Campos JL, Mongkolsapaya J, Screaton GR. The immune response against flaviviruses. Nat Immunol 2018; 19: 1189-1198.
3. Londoño-Renteria B, Colpitts TM. A brief review of West Nile virus biology. Methods Mol Biol 2016; 1435: 1-13.
4. Young JJ, Haussig JM, Aberle SW, Pervanidou D, Riccardo F, sekulic N, et al. Epidemiology of human West Nile virus infections in the European Union and European Union enlargement countries, 2010 to 2018. Euro Surveill 2021; 26: 2001095.
5. Kardani K, Bolhassani A, Namvar A. An overview of in silico vaccine design against different pathogens and cancer. Expert Rev Vaccines 2020; 19: 699-726.
6. Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG, et al. Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res 2003; 31: 3497-3500.
7. Yao B, Zhang L, Liang S, Zhang C. SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PLoS One 2012; 7(9): e45152.
8. El-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels. J Mol Recognit 2008; 21: 243-255.
9. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 2017; 45(W1): W24-W29.
10. Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 2019; 47(D1): D339-D343.
11. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007; 8: 4.
12. Magnan CN, Zeller M, Kayala MA, Vigil A, Randall A, Felgner PL, et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 2010; 26: 2936-2943.
13. Dimitrov I, Flower DR, Doytchinova I. AllerTOP—a server for in silico prediction of allergens. BMC Bioinformatics 2013; 14 Suppl 6(Suppl 6): S4.
14. Saha S, Raghava GPS. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 2006; 34(Web Server issue): W202-W209.
15. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 2003; 31: 3784-3788.
16. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001; 305: 567-580.
17. Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 2019; 37: 420-423.
18. Kloczkowski A, Ting KL, Jernigan RL, Garnier J. Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence. Proteins 2002; 49: 154-166.
19. Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, et al. Template-based protein structure modeling using the RaptorX web server. Nat Protoc 2012; 7: 1511-1522.
20. Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res 2013; 41(Web Server issue): W384-W388.
21. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007; 35(Web Server issue): W407-W410.
22. Yang YD, Spratt P, Chen H, Park C, Kihara D. Sub-AQUA: real-value quality assessment of protein structure models. Protein Eng Des Sel 2010; 23: 617-632.
23. Puigbo P, Guzman E, Romeu A, Garcia-Vallve S. OPTIMIZER: a web server for optimizing the codon usage of DNA sequences. Nucleic Acids Res 2007; 35(Web Server issue): W126-W131.
24. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL. The Vienna RNA websuite. Nucleic Acids Res 2008; 36(Web Server issue): W70-W74.
25. Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, et al. The ClusPro web server for protein-protein docking. Nat Protoc 2017; 12: 255-278.
26. Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016; 32: 3676-3678.
27. Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: structural summaries of PDB entries. Protein Sci 2018; 27: 129-134.
28. De Filette M, Chabierski S, Andries O, Ulbert S, Sanders NN. T cell epitope mapping of the E-protein of West Nile virus in BALB/c mice. PLoS One 2014; 9(12): e115343.
29. Diamond MS, Pierson TC, Fremont DH. The structural immunology of antibody protection against West Nile virus. Immunol Rev 2008; 225: 212-225.
30. Chung KM, Nybakken GE, Thompson BS, Engle MJ, Marri A, Fremont DH, et al. Antibodies against West Nile virus nonstructural protein NS1 prevent lethal infection through Fc γ receptor-dependent and -independent mechanisms. J Virol 2006; 80: 1340-1351.
31. Salvador EA, Pires de Souza GA, Cotta Malaquias LC, Wang T, Leomil Coelho LF. Identification of relevant regions on structural and nonstructural proteins of Zika virus for vaccine and diagnostic test development: an in silico approach. New Microbes New Infect 2019; 29: 100506.
32. Daffis S, Samuel MA, Suthar MS, Keller BC, Gale M Jr, Diamond MS. Toll-like receptor 3 has a protective role against West Nile virus infection. J Virol 2008; 82: 10349-10358.
33. Netland J, Bevan MJ. CD8 and CD4 T cells in West Nile virus immunity and pathogenesis. Viruses 2013; 5: 2573-2584.
34. Shrestha B, Wang T, Samuel MA, Whitby K, Craft J, Fikrig E, et al. Gamma interferon plays a crucial early antiviral role in protection against West Nile virus infection. J Virol 2006; 80: 5338-5348.
35. Acharya D, Wang P, Paul AM, Dai J, Gate D, Lowery JE, et al. Interleukin-17A promotes CD8+ T cell cytotoxicity to facilitate West Nile virus clearance. J Virol 2017; 91(1): e01529-16.
36. Schneeweiss A, Chabierski S, Salomo M, Delaroque N, Al-Robaiy S, Grunwald T, et al. A DNA vaccine encoding the E protein of West Nile virus is protective and can be boosted by recombinant domain DIII. Vaccine 2011; 29: 6352-6357.
37. Nelson MH, Winkelmann E, Ma Y, Xia J, Mason PW, Bourne N, et al. Immunogenicity of RepliVAX WN, a novel single-cycle West Nile virus vaccine. Vaccine 2010; 29: 174-182.
| Files | ||
| Issue | Vol 18 No 5 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijm.v18i5.22876 | |
| Keywords | ||
| West Nile virus Immunogenicity Epitopes Recombinant fusion proteins | ||
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How to Cite
1.
Arezoumandi M, Shojaei Baghini S, Rad N, Taghipour E, Pouriayevali MH, Frootan F, Motallebi M, Salmanian AH. Evaluation of the immunogenic properties of a chimeric protein against West Nile virus: in-silico analysis and assessment in mouse. Iran J Microbiol. 2026;18(5):861-871.
