Genomics & Informatics

  • 제20권1호
  • /
  • Pages.11.1-11.20
  • /
  • 2022
  • /
  • 1598-866X(pISSN)
  • /
  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
권호 표지
DOI QR코드

DOI QR Code

Designing a novel mRNA vaccine against Vibrio harveyi infection in fish: an immunoinformatics approach

  • Islam, Sk Injamamul (Department of Fisheries and Marine Bioscience, Faculty of Biological Science, Jashore University of Science and Technology) ;
  • Mou, Moslema Jahan (Department of Genetic Engineering & Biotechnology, Faculty of Earth and Life Science, University of Rajshahi) ;
  • Sanjida, Saloa (Department of Environmental Science and Technology, Faculty of Applied Science and Technology, Jashore University of Science and Technology) ;
  • Tariq, Muhammad (Department of Biotechnology, Faculty of Biological Sciences, University of Malakand) ;
  • Nasir, Saad (Department of Clinical Medicine and Surgery, Faculty of Veterinary Medicine, University of Veterinary and Animal Sciences) ;
  • Mahfuj, Sarower (Department of Fisheries and Marine Bioscience, Faculty of Biological Science, Jashore University of Science and Technology)
  • 투고 : 2021.11.01
  • 심사 : 2022.01.07
  • 발행 : 2022.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Vibrio harveyi belongs to the Vibrio genus that causes vibriosis in marine and aquatic fish species through double-stranded DNA virus replication. In humans, around 12 Vibrio species can cause gastroenteritis (gastrointestinal illness). A large amount of virus particles can be found in the cytoplasm of infected cells, which may cause death. Despite these devastating complications, there is still no cure or vaccine for the virus. As a result, we used an immunoinformatics approach to develop a multi-epitope vaccine against most pathogenic hemolysin gene of V. harveyi. The immunodominant T- and B-cell epitopes were identified using the hemolysin protein. We developed a vaccine employing three possible epitopes: cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocyte epitopes, after thorough testing. The vaccine was developed to be antigenic, immunogenic, and non-allergenic, as well as having a better solubility. Molecular dynamics simulation revealed significant structural stiffness and binding stability. In addition, the immunological simulation generated by computer revealed that the vaccination might elicit immune reactions in the actual life after injection. Finally, using Escherichia coli K12 as a model, codon optimization yielded ideal GC content and a higher codon adaptation index value, which was then included in the cloning vector pET2+ (a). Altogether, our experiment implies that the proposed peptide vaccine might be a good option for vibriosis prophylaxis.

키워드

과제정보

The author thanks Dr. Foysal Ahmed Sagore and Dr. Kazi Abdus Samad for helpful comments.

참고문헌

  1. Bunpa S, Sermwittayawong N, Vuddhakul V. Extracellular enzymes produced by Vibrio alginolyticus isolated from environments and diseased aquatic animals. Procedia Chem 2016;18:12-17. https://doi.org/10.1016/j.proche.2016.01.002
  2. Xu Y, Wang C, Zhang G, Tian J, Liu Y, Shen X, et al. ISCR2 is associated with the dissemination of multiple resistance genes among Vibrio spp. and Pseudoalteromonas spp. isolated from farmed fish. Arch Microbiol 2017;199:891-896. https://doi.org/10.1007/s00203-017-1365-2
  3. Khouadja S, Lamari F, Bakhrouf A. Characterization of Vibrio parahaemolyticus isolated from farmed sea bass (Dicentrarchus labrax) during disease outbreaks. Int Aquat Res 2013;5:13. https://doi.org/10.1186/2008-6970-5-13
  4. Abdullah A, Ramli R, Ridzuan MS, Murni M, Hashim S, Sudirwan F, et al. The presence of Vibrionaceae, Betanodavirus and Iridovirus in marine cage-cultured fish: role of fish size, water physicochemical parameters and relationships among the pathogens. Aquac Rep 2017;7:57-65. https://doi.org/10.1016/j.aqrep.2017.06.001
  5. Dong HT, Taengphu S, Sangsuriya P, Charoensapsri W, Phiwsaiya K, Sornwatana T, et al. Recovery of Vibrio harveyi from scale drop and muscle necrosis disease in farmed barramundi, Lates calcarifer in Vietnam. Aquaculture 2017;473:89-96. https://doi.org/10.1016/j.aquaculture.2017.02.005
  6. Mohamad N, Mohd Roseli FA, Azmai MN, Saad MZ, Md Yasin IS, Zulkiply NA, et al. Natural concurrent infection of Vibrio harveyi and V. alginolyticus in cultured hybrid groupers in Malaysia. J Aquat Anim Health 2019;31:88-96. https://doi.org/10.1002/aah.10055
  7. Haldar S, Maharajan A, Chatterjee S, Hunter SA, Chowdhury N, Hinenoya A, et al. Identification of Vibrio harveyi as a causative bacterium for a tail rot disease of sea bream Sparus aurata from research hatchery in Malta. Microbiol Res 2010;165:639-648. https://doi.org/10.1016/j.micres.2009.12.001
  8. Cabello FC, Godfrey HP, Tomova A, Ivanova L, Dolz H, Millanao A, et al. Antimicrobial use in aquaculture re-examined: its relevance to antimicrobial resistance and to animal and human health. Environ Microbiol 2013;15:1917-1942. https://doi.org/10.1111/1462-2920.12134
  9. Thirugnanasambandam R, Inbakandan D, Kumar C, Subashni B, Vasantharaja R, Stanley Abraham L, et al. Genomic insights of Vibrio harveyi RT-6 strain, from infected "Whiteleg shrimp" (Litopenaeus vannamei) using Illumina platform. Mol Phylogenet Evol 2019;130:35-44. https://doi.org/10.1016/j.ympev.2018.09.015
  10. Austin B, Zhang XH. Vibrio harveyi: a significant pathogen of marine vertebrates and invertebrates. Lett Appl Microbiol 2006;43:119-124. https://doi.org/10.1111/j.1472-765X.2006.01989.x
  11. Zhang XH, He X, Austin B. Vibrio harveyi: a serious pathogen of fish and invertebrates in mariculture. Mar Life Sci Technol 2020;2:231-245. https://doi.org/10.1007/s42995-020-00037-z
  12. Leal Y, Velazquez J, Hernandez L, Swain JK, Rodriguez AR, Martinez R, et al. Promiscuous T cell epitopes boosts specific IgM immune response against a P0 peptide antigen from sea lice in different teleost species. Fish Shellfish Immunol 2019;92:322-330. https://doi.org/10.1016/j.fsi.2019.06.018
  13. Ashfaq H, Soliman H, Fajmann S, Sexl V, El-Matbouli M, Saleh M. Kinetics of CD4-1+ lymphocytes in brown trout after exposure to viral haemorrhagic septicaemia virus. J Fish Dis 2021;44:1553-1562. https://doi.org/10.1111/jfd.13476
  14. Nakanishi T, Fischer U, Dijkstra JM, Hasegawa S, Somamoto T, Okamoto N, et al. Cytotoxic T cell function in fish. Dev Comp Immunol 2002;26:131-139. https://doi.org/10.1016/S0145-305X(01)00055-6
  15. Adams A. Progress, challenges and opportunities in fish vaccine development. Fish Shellfish Immunol 2019;90:210-214. https://doi.org/10.1016/j.fsi.2019.04.066
  16. Munoz-Medina JE, Sanchez-Vallejo CJ, Mendez-Tenorio A, Monroy-Munoz IE, Angeles-Martinez J, Santos Coy-Arechavaleta A, et al. In silico identification of highly conserved epitopes of influenza A H1N1, H2N2, H3N2, and H5N1 with diagnostic and vaccination potential. Biomed Res Int 2015;2015:813047. https://doi.org/10.1155/2015/813047
  17. Ali MT, Morshed MM, Hassan F. A computational approach for designing a universal epitope-based peptide vaccine against Nipah virus. Interdiscip Sci 2015;7:177-185. https://doi.org/10.1007/s12539-015-0023-0
  18. Anwar S, Mourosi JT, Khan MF, Hosen MJ. Prediction of epitope-based peptide vaccine against the Chikungunya virus by immuno-informatics approach. Curr Pharm Biotechnol 2020;21:325-340. https://doi.org/10.2174/1389201020666191112161743
  19. Dash R, Das R, Junaid M, Akash MF, Islam A, Hosen SZ. In silico-based vaccine design against Ebola virus glycoprotein. Adv Appl Bioinform Chem 2017;10:11-28. https://doi.org/10.2147/AABC.S115859
  20. Shi J, Zhang J, Li S, Sun J, Teng Y, Wu M, et al. Epitope-based vaccine target screening against highly pathogenic MERS-CoV: an in silico approach applied to emerging infectious diseases. PLoS One 2015;10:e0144475. https://doi.org/10.1371/journal.pone.0144475
  21. Grimholt U. MHC and evolution in teleosts. Biology (Basel) 2016;5:6. https://doi.org/10.3390/biology5010006
  22. Dijkstra JM, Grimholt U, Leong J, Koop BF, Hashimoto K. Comprehensive analysis of MHC class II genes in teleost fish genomes reveals dispensability of the peptide-loading DM system in a large part of vertebrates. BMC Evol Biol 2013;13:260. https://doi.org/10.1186/1471-2148-13-260
  23. Yamaguchi T, Dijkstra JM. Major histocompatibility complex (MHC) genes and disease resistance in fish. Cells 2019;8:378. https://doi.org/10.3390/cells8040378
  24. Stosik M, Tokarz-Deptula B, Deptula W. Major histocompatibility complex in osteichthyes. J Vet Res 2020;64:127-136. https://doi.org/10.2478/jvetres-2020-0025
  25. Bolnick DI, Snowberg LK, Caporaso JG, Lauber C, Knight R, Stutz WE. Major histocompatibility complex class IIb polymorphism influences gut microbiota composition and diversity. Mol Ecol 2014;23:4831-4845. https://doi.org/10.1111/mec.12846
  26. Marana MH, Jorgensen LV, Skov J, Chettri JK, Holm Mattsson A, Dalsgaard I, et al. Subunit vaccine candidates against Aeromonas salmonicida in rainbow trout Oncorhynchus mykiss. PLoS One 2017;12:e0171944. https://doi.org/10.1371/journal.pone.0171944
  27. Mahendran R, Jeyabaskar S, Sitharaman G, Michael RD, Paul AV. Computer-aided vaccine designing approach against fish pathogens Edwardsiella tarda and Flavobacterium columnare using bioinformatics softwares. Drug Des Devel Ther 2016;10:1703-1714.
  28. Pereira UP, Soares SC, Blom J, Leal CA, Ramos RT, Guimaraes LC, et al. In silico prediction of conserved vaccine targets in Streptococcus agalactiae strains isolated from fish, cattle, and human samples. Genet Mol Res 2013;12:2902-2912. https://doi.org/10.4238/2013.August.12.6
  29. Pumchan A, Krobthong S, Roytrakul S, Sawatdichaikul O, Kondo H, Hirono I, et al. Novel chimeric multiepitope vaccine for streptococcosis disease in Nile Tilapia (Oreochromis niloticus Linn.). Sci Rep 2020;10:603. https://doi.org/10.1038/s41598-019-57283-0
  30. Madonia A, Melchiorri C, Bonamano S, Marcelli M, Bulfon C, Castiglione F, et al. Computational modeling of immune system of the fish for a more effective vaccination in aquaculture. Bioinformatics 2017;33:3065-3071. https://doi.org/10.1093/bioinformatics/btx341
  31. Joshi A, Pathak DC, Mannan MA, Kaushik V. In-silico designing of epitope-based vaccine against the seven banded grouper nervous necrosis virus affecting fish species. Netw Model Anal Health Inform Bioinform 2021;10:37. https://doi.org/10.1007/s13721-021-00315-5
  32. Throngnumchai B, Jitrakorn S, Sangsuriya P, Unajak S, Khunrae P, Dong HT, et al. Refolded recombinant major capsid protein (MCP) from Infectious Spleen and Kidney Necrosis Virus (ISKNV) effectively stimulates serum specific antibody and immune related genesresponse in Nile tilapia (Oreochromis niloticus). Protein Expr Purif 2021;184:105876. https://doi.org/10.1016/j.pep.2021.105876
  33. Dong C, Xiong X, Luo Y, Weng S, Wang Q, He J. Efficacy of a formalin-killed cell vaccine against infectious spleen and kidney necrosis virus (ISKNV) and immunoproteomic analysis of its major immunogenic proteins. Vet Microbiol 2013;162:419-428. https://doi.org/10.1016/j.vetmic.2012.10.026
  34. Yuan Y, Feng Z, Wang J. Vibrio vulnificus hemolysin: biological activity, regulation of vvhA expression, and role in pathogenesis. Front Immunol 2020;11:599439. https://doi.org/10.3389/fimmu.2020.599439
  35. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007;8:4. https://doi.org/10.1186/1471-2105-8-4
  36. 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. https://doi.org/10.1093/bioinformatics/btq551
  37. Farhood B, Najafi M, Mortezaee K. CD8(+) cytotoxic T lymphocytes in cancer immunotherapy: a review. J Cell Physiol 2019;234:8509-8521. https://doi.org/10.1002/jcp.27782
  38. Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 2007;8:424. https://doi.org/10.1186/1471-2105-8-424
  39. Calis JJ, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 2013;9:e1003266. https://doi.org/10.1371/journal.pcbi.1003266
  40. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R; Open Source Drug Discovery Cnsortium, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One 2013;8:e73957. https://doi.org/10.1371/journal.pone.0073957
  41. Dimitrov I, Flower DR, Doytchinova I. AllerTOP: a server for in silico prediction of allergens. BMC Bioinformatics 2013;14 Suppl 6:S4. https://doi.org/10.1186/1471-2105-14-S6-S4
  42. Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 2020;8:420-422. https://doi.org/10.1016/s2213-2600(20)30076-x
  43. Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 2018;154:394-406. https://doi.org/10.1111/imm.12889
  44. Moutaftsi M, Peters B, Pasquetto V, Tscharke DC, Sidney J, Bui HH, et al. A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol 2006;24:817-819. https://doi.org/10.1038/nbt1215
  45. Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct 2013;8:30. https://doi.org/10.1186/1745-6150-8-30
  46. Nagpal G, Usmani SS, Dhanda SK, Kaur H, Singh S, Sharma M, et al. Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci Rep 2017;7:42851. https://doi.org/10.1038/srep42851
  47. Nain Z, Abdulla F, Rahman MM, Karim MM, Khan MS, Sayed SB, et al. Proteome-wide screening for designing a multi-epitope vaccine against emerging pathogen Elizabethkingia anophelis using immunoinformatic approaches. J Biomol Struct Dyn 2020;38:4850-4867. https://doi.org/10.1080/07391102.2019.1692072
  48. Manavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G. iBCEEL: a new ensemble learning framework for improved linear B-cell epitope prediction. Front Immunol 2018;9:1695. https://doi.org/10.3389/fimmu.2018.01695
  49. Latysheva NS, Babu MM. Discovering and understanding oncogenic gene fusions through data intensive computational approaches. Nucleic Acids Res 2016;44:4487-4503. https://doi.org/10.1093/nar/gkw282
  50. Chen X, Zaro JL, Shen WC. Fusion protein linkers: property, design and functionality. Adv Drug Deliv Rev 2013;65:1357-1369. https://doi.org/10.1016/j.addr.2012.09.039
  51. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-461. https://doi.org/10.1002/jcc.21334
  52. Dorosti H, Eslami M, Negahdaripour M, Ghoshoon MB, Gholami A, Heidari R, et al. Vaccinomics approach for developing multi-epitope peptide pneumococcal vaccine. J Biomol Struct Dyn 2019;37:3524-3535. https://doi.org/10.1080/07391102.2018.1519460
  53. Nain Z, Karim MM, Sen MK, Adhikari UK. Structural basis and designing of peptide vaccine using PE-PGRS family protein of Mycobacterium ulcerans: an integrated vaccinomics approach. Mol Immunol 2020;120:146-163. https://doi.org/10.1016/j.molimm.2020.02.009
  54. Olejnik J, Hume AJ, Muhlberger E. Toll-like receptor 4 in acute viral infection: too much of a good thing. PLoS Pathog 2018;14:e1007390. https://doi.org/10.1371/journal.ppat.1007390
  55. Pandey RK, Bhatt TK, Prajapati VK. Novel immunoinformatics approaches to design multi-epitope subunit vaccine for malaria by investigating anopheles salivary protein. Sci Rep 2018;8:1125. https://doi.org/10.1038/s41598-018-19456-1
  56. Abdellrazeq GS, Fry LM, Elnaggar MM, Bannantine JP, Schneider DA, Chamberlin WM, et al. Simultaneous cognate epitope recognition by bovine CD4 and CD8 T cells is essential for primary expansion of antigen-specific cytotoxic T-cells following ex vivo stimulation with a candidate Mycobacterium avium subsp. paratuberculosis peptide vaccine. Vaccine 2020;38:2016-2025. https://doi.org/10.1016/j.vaccine.2019.12.052
  57. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol 1999;112:531-552.
  58. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 1995;11:681-684.
  59. Buchan DW, Minneci F, Nugent TC, Bryson K, Jones DT. Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res 2013;41:W349-W357. https://doi.org/10.1093/nar/gkt381
  60. Xu J, McPartlon M, Li J. Improved protein structure prediction by deep learning irrespective of co-evolution information. Nat Mach Intell 2021;3:601-609. https://doi.org/10.1038/s42256-021-00348-5
  61. Nugent T, Cozzetto D, Jones DT. Evaluation of predictions in the CASP10 model refinement category. Proteins 2014;82 Suppl 2:98-111. https://doi.org/10.1002/prot.24377
  62. DeLano WL. PyMOL: an open-source molecular graphics tool. CCP4 Newsl Protein Crystallogr 2002;40:82-92.
  63. Lovell SC, Davis IW, Arendall WB, de Bakker PI, Word JM, Prisant MG, et al. Structure validation by Calpha geometry: phi,psi and Cbeta deviation. Proteins 2003;50:437-450. https://doi.org/10.1002/prot.10286
  64. 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:W407-W410.
  65. 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. https://doi.org/10.1038/nprot.2016.169
  66. Pokhrel S, Bouback TA, Samad A, Nur SM, Alam R, Abdullah-Al-Mamun M, et al. Spike protein recognizer receptor ACE2 targeted identification of potential natural antiviral drug candidates against SARS-CoV-2. Int J Biol Macromol 2021;191:1114-1125. https://doi.org/10.1016/j.ijbiomac.2021.09.146
  67. Bouback TA, Pokhrel S, Albeshri A, Aljohani AM, Samad A, Alam R, et al. Pharmacophore-based virtual screening, quantum mechanics calculations, and molecular dynamics simulation approaches identified potential natural antiviral drug candidates against MERS-CoV S1-NTD. Molecules 2021;26:4961. https://doi.org/10.3390/molecules26164961
  68. Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 2010;5:e9862. https://doi.org/10.1371/journal.pone.0009862
  69. Castiglione F, Mantile F, De Berardinis P, Prisco A. How the interval between prime and boost injection affects the immune response in a computational model of the immune system. Comput Math Methods Med 2012;2012:842329.
  70. Grote A, Hiller K, Scheer M, Munch R, Nortemann B, Hempel DC, et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 2005;33:W526-W531. https://doi.org/10.1093/nar/gki376
  71. Goldberg MF, Roeske EK, Ward LN, Pengo T, Dileepan T, Kotov DI, et al. Salmonella persist in activated macrophages in T cellsparse granulomas but are contained by surrounding CXCR3 ligand-positioned Th1 cells. Immunity 2018;49:1090-1102. https://doi.org/10.1016/j.immuni.2018.10.009
  72. Liu X, Sun W, Zhang Y, Zhou Y, Xu J, Gao X, et al. Impact of Aeromonas hydrophila and infectious spleen and kidney necrosis virus infections on susceptibility and host immune response in Chinese perch (Siniperca chuatsi). Fish Shellfish Immunol 2020;105:117-125. https://doi.org/10.1016/j.fsi.2020.07.012
  73. Li W, Joshi MD, Singhania S, Ramsey KH, Murthy AK. Peptide vaccine: progress and challenges. Vaccines (Basel) 2014;2:515-536. https://doi.org/10.3390/vaccines2030515
  74. Bol KF, Aarntzen EH, Pots JM, Olde Nordkamp MA, van de Rakt MW, Scharenborg NM, et al. Prophylactic vaccines are potent activators of monocyte-derived dendritic cells and drive effective anti-tumor responses in melanoma patients at the cost of toxicity. Cancer Immunol Immunother 2016;65:327-339. https://doi.org/10.1007/s00262-016-1796-7
  75. Shamriz S, Ofoghi H, Moazami N. Effect of linker length and residues on the structure and stability of a fusion protein with malaria vaccine application. Comput Biol Med 2016;76:24-29. https://doi.org/10.1016/j.compbiomed.2016.06.015
  76. Bonam SR, Partidos CD, Halmuthur SK, Muller S. An overview of novel adjuvants designed for improving vaccine efficacy. Trends Pharmacol Sci 2017;38:771-793. https://doi.org/10.1016/j.tips.2017.06.002
  77. Khatoon N, Pandey RK, Prajapati VK. Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Sci Rep 2017;7:8285. https://doi.org/10.1038/s41598-017-08842-w