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Immunoinformatics approaches in developing a novel multi-epitope chimeric vaccine protective against Saprolegnia parasitica – Scientific Reports

  • Kar, D. Chapter 1—Introduction. In Epizootic Ulcerative Fish Disease Syndrome (ed. Kar, D.) 1–19 (Academic Press, 2016).


    Google Scholar
     

  • Chong, R.S.-M. Chapter 53—Saprolegniasis. In Aquaculture Pathophysiology (eds Kibenge, F. S. B. et al.) 645–650 (Academic Press, 2022).

    Chapter 

    Google Scholar
     

  • Pavić, D. et al. Tracing the oomycete pathogen Saprolegnia parasitica in aquaculture and the environment. Sci. Rep. 12, 16646. https://doi.org/10.1038/s41598-022-16553-0 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lone, S. & Manohar, S. Saprolegnia parasitica, a lethal oomycete pathogen: Demands to be controlled. J. Infect. Mol. Biol. 6, 44. https://doi.org/10.17582/journal.jimb/2018/6.2.36.44 (2018).

    Article 

    Google Scholar
     

  • Buchmann, K., James, B., Dalvin, S., Øvergård, A. C. & Vendramin, N. (Consejo Superior de Investigaciones Científicas (España), 2020).

  • Ortega, C., Fernandez, A. B., Muzquiz, J. L., Ania, S. & Gimeno, O. Health risks associated with the migration of Atlantic salmon (Salmo salar L.): An epidemiological surveillance programme in Northern Spain. Rev. Sci. Tech. 24, 887–898 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Ciepliński, M., Kasprzak, M., Grandtke, M., Giertych, M. J. & Steliga, A. Pattern of secondary infection with spp. in wild spawners of UDN-affected sea trout Salmo trutta m. (L.), the Słupia River, N Poland. Oceanol. Hydrobiol. Stud. 47, 230–238. https://doi.org/10.1515/ohs-2018-0022 (2018).

    Article 

    Google Scholar
     

  • Whipps, C. M. & Kent, M. L. Chapter 41—Bacterial and fungal diseases of zebrafish. In The Zebrafish in Biomedical Research (eds Cartner, S. C. et al.) 495–508 (Academic Press, 2020).

    Chapter 

    Google Scholar
     

  • Willoughby, L. G. & Roberts, R. J. Towards strategic use of fungicides against Saprolegnia parasitica in salmonid fish hatcheries. J. Fish Dis. 15, 1–13. https://doi.org/10.1111/j.1365-2761.1992.tb00631.x (1992).

    Article 

    Google Scholar
     

  • Alderman, D. J. Malachite green: A review. J. Fish Dis. 8, 289–298. https://doi.org/10.1111/j.1365-2761.1985.tb00945.x (1985).

    Article 

    Google Scholar
     

  • Srivastava, S., Sinha, R. & Roy, D. Toxicological effects of malachite green. Aquat. Toxicol. 66, 319–329. https://doi.org/10.1016/j.aquatox.2003.09.008 (2004).

    Article 
    PubMed 

    Google Scholar
     

  • Culp, S. J. et al. Carcinogenicity of malachite green chloride and leucomalachite green in B6C3F1 mice and F344 rats. Food Chem. Toxicol. 44, 1204–1212. https://doi.org/10.1016/j.fct.2006.01.016 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • He, J., Mo, P., Luo, Y.-S. & Yang, P.-H. Strategies for solving the issue of malachite green residues in aquatic products: A review. Aquacult. Res. 2023, 8578570. https://doi.org/10.1155/2023/8578570 (2023).

    Article 

    Google Scholar
     

  • Pipoyan, D., Stepanyan, S., Beglaryan, M., Stepanyan, S. & Mantovani, A. Health risk assessment of toxicologically relevant residues in emerging countries: A pilot study on Malachite Green residues in farmed freshwater fish of Armenia. Food Chem. Toxicol. 143, 111526. https://doi.org/10.1016/j.fct.2020.111526 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Andersson, M. G. & Cerenius, L. Pumilio homologue from Saprolegnia parasitica specifically expressed in undifferentiated spore cysts. Eukaryotic Cell 1, 105–111. https://doi.org/10.1128/ec.1.1.105-111.2002 (2002).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Matthews, E., Ellison, A. & Cable, J. Saprolegnia parasitica zoospore activity and host survival indicates isolate variation in host preference. Fungal Biol. 125, 260–268. https://doi.org/10.1016/j.funbio.2020.11.002 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Robertson, E. J. et al. Oomycete Genetics and Genomics 407–424 (Springer, 2009).

    Book 

    Google Scholar
     

  • Diéguez-Uribeondo, J., Cerenius, L. & Söderhäll, K. Repeated zoospore emergence in Saprolegnia parasitica. Mycol. Res. 98, 810–815. https://doi.org/10.1016/S0953-7562(09)81060-5 (1994).

    Article 

    Google Scholar
     

  • Wawra, S. et al. Host-targeting protein 1 (SpHtp1) from the oomycete Saprolegnia parasitica translocates specifically into fish cells in a tyrosine-O-sulphate-dependent manner. Proc. Natl. Acad. Sci. 109, 2096–2101. https://doi.org/10.1073/pnas.1113775109 (2012).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Trusch, F. et al. Cell entry of a host-targeting protein of oomycetes requires gp96. Nat. Commun. 9, 2347. https://doi.org/10.1038/s41467-018-04796-3 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rezinciuc, S., Sandoval-Sierra, J. V., Ruiz-León, Y., van West, P. & Diéguez-Uribeondo, J. Specialized attachment structure of the fish pathogenic oomycete Saprolegnia parasitica. PLoS ONE 13, e0190361. https://doi.org/10.1371/journal.pone.0190361 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Srivastava, V., Rezinciuc, S. & Bulone, V. Quantitative proteomic analysis of four developmental stages of Saprolegnia parasitica. Front. Microbiol. 8, 2658. https://doi.org/10.3389/fmicb.2017.02658 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Kumar, S., Mandal, R. S., Bulone, V. & Srivastava, V. Identification of growth inhibitors of the fish pathogen Saprolegnia parasitica using in silico subtractive proteomics, computational modeling, and biochemical validation. Front. Microbiol. 11, 571093. https://doi.org/10.3389/fmicb.2020.571093 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kuang, G., Bulone, V. & Tu, Y. Computational studies of the binding profile of phosphoinositide PtdIns (3,4,5) P3 with the pleckstrin homology domain of an oomycete cellulose synthase. Sci. Rep. 6, 20555. https://doi.org/10.1038/srep20555 (2016).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Saldanha, L., Langel, Ü. & Vale, N. In silico studies to support vaccine development. Pharmaceutics 15, 654 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vita, R. et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 47, D339–D343. https://doi.org/10.1093/nar/gky1006 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: Application to the MHC class I system. Bioinformatics 32, 511–517. https://doi.org/10.1093/bioinformatics/btv639 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Nielsen, M. & Lund, O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinform. 10, 296. https://doi.org/10.1186/1471-2105-10-296 (2009).

    Article 

    Google Scholar
     

  • Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 4. https://doi.org/10.1186/1471-2105-8-4 (2007).

    Article 

    Google Scholar
     

  • Doytchinova, I. & Flower, D. Bioinformatic approach for identifying parasite and fungal candidate subunit vaccines. Open Vaccine J. 1, 22–26. https://doi.org/10.2174/1875035400801010022 (2008).

    Article 

    Google Scholar
     

  • Shey, R. A. et al. In-silico design of a multi-epitope vaccine candidate against onchocerciasis and related filarial diseases. Sci. Rep. 9, 4409. https://doi.org/10.1038/s41598-019-40833-x (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, X., Zaro, J. L. & Shen, W.-C. Fusion protein linkers: Property, design and functionality. Adv. Drug Deliv. Rev. 65, 1357–1369. https://doi.org/10.1016/j.addr.2012.09.039 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Mittal, A., Sasidharan, S., Raj, S., Balaji, S. N. & Saudagar, P. Exploring the zika genome to design a potential multiepitope vaccine using an immunoinformatics approach. Int. J. Peptide Res. Therap. 26, 2231–2240. https://doi.org/10.1007/s10989-020-10020-y (2020).

    Article 

    Google Scholar
     

  • Yano, A. et al. An ingenious design for peptide vaccines. Vaccine 23, 2322–2326. https://doi.org/10.1016/j.vaccine.2005.01.031 (2005).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Gu, Y. et al. Vaccination with a paramyosin-based multi-epitope vaccine elicits significant protective immunity against Trichinella spiralis infection in mice. Front. Microbiol. 8, 475. https://doi.org/10.3389/fmicb.2017.01475 (2017).

    Article 

    Google Scholar
     

  • Yang, Y. et al. In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Hum. Vaccines Immunotherap. 11, 795–805. https://doi.org/10.1080/21645515.2015.1012017 (2015).

    Article 

    Google Scholar
     

  • Sarkar, B., Ullah, M. A., Johora, F. T., Taniya, M. A. & Araf, Y. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS coronavirus-2 (SARS-CoV-2). Immunobiology 225, 151955. https://doi.org/10.1016/j.imbio.2020.151955 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Livingston, B. et al. A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes1. J. Immunol. 168, 5499–5506. https://doi.org/10.4049/jimmunol.168.11.5499 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Mansoor, S., Baek, M., Juergens, D., Watson, J. L. & Baker, D. Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold. Protein Sci. 32, e4780. https://doi.org/10.1002/pro.4780 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lahiri, T., Singh, K., Pal, M. K. & Verma, G. Protein structure validation using a semi-empirical method. Bioinformation 8(20), 984–987. https://doi.org/10.6026/97320630008984 (2012).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kiefer, F., Arnold, K., Künzli, M., Bordoli, L. & Schwede, T. The SWISS-MODEL repository and associated resources. Nucleic Acids Res. 37, D387–D392. https://doi.org/10.1093/nar/gkn750 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Wiederstein, M. & Sippl, M. J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35, W407–W410. https://doi.org/10.1093/nar/gkm290 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Garg, V. K. et al. MFPPI—Multi FASTA ProtParam interface. Bioinformation 12(2), 74–77. https://doi.org/10.6026/97320630012074 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dimitrov, I., Bangov, I., Flower, D. R. & Doytchinova, I. AllerTOP v.2—A server for in silico prediction of allergens. J. Mol. Model. 20, 2278. https://doi.org/10.1007/s00894-014-2278-5 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Garg, A. & Gupta, D. VirulentPred: A SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinform. 9, 62. https://doi.org/10.1186/1471-2105-9-62 (2008).

    Article 

    Google Scholar
     

  • Hebditch, M., Carballo-Amador, M. A., Charonis, S., Curtis, R. & Warwicker, J. Protein–Sol: A web tool for predicting protein solubility from sequence. Bioinformatics 33, 3098–3100. https://doi.org/10.1093/bioinformatics/btx345 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ponomarenko, J. et al. ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 9, 514. https://doi.org/10.1186/1471-2105-9-514 (2008).

    Article 

    Google Scholar
     

  • Rødland, E. K., Ager-Wick, E., Halvorsen, B., Müller, F. & Frøland, S. S. Toll like receptor 5 (TLR5) may be involved in the immunological response to Aspergillus fumigatus in vitro. Med. Mycol. 49, 375–379. https://doi.org/10.3109/13693786.2010.531772 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Akhtar, N., Joshi, A., Kaushik, V., Kumar, M. & Mannan, M.A.-U. In-silico design of a multivalent epitope-based vaccine against Candida auris. Microbial Pathog. 155, 104879. https://doi.org/10.1016/j.micpath.2021.104879 (2021).

    Article 

    Google Scholar
     

  • Akhtar, N., Singh, A., Upadhyay, A. K. & Mannan, M.A.-U. Design of a multi-epitope vaccine against the pathogenic fungi Candida tropicalis using an in silico approach. J. Genet. Eng. Biotechnol. 20, 140. https://doi.org/10.1186/s43141-022-00415-3 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kozakov, D. et al. The ClusPro web server for protein–protein docking. Nat. Protoc. 12, 255–278. https://doi.org/10.1038/nprot.2016.169 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yuan, S., Chan, H. C. S. & Hu, Z. Using PyMOL as a platform for computational drug design. WIREs Comput. Mol. Sci. 7, e1298. https://doi.org/10.1002/wcms.1298 (2017).

    Article 

    Google Scholar
     

  • Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1, 19–25. https://doi.org/10.1016/j.softx.2015.06.001 (2015).

    Article 
    ADS 

    Google Scholar
     

  • Van Der Spoel, D. et al. GROMACS: Fast, flexible, and free. J. Comput. Chem. 26, 1701–1718. https://doi.org/10.1002/jcc.20291 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Witeska, M., Kondera, E., Ługowska, K. & Bojarski, B. Hematological methods in fish—Not only for beginners. Aquaculture 547, 737498. https://doi.org/10.1016/j.aquaculture.2021.737498 (2022).

    Article 

    Google Scholar
     

  • Olson, K. R. & Hoagland, T. M. Effects of freshwater and saltwater adaptation and dietary salt on fluid compartments, blood pressure, and venous capacitance in trout. Am. J. Physiol. Regul. Integr. Compar. Physiol. 294, R1061–R1067. https://doi.org/10.1152/ajpregu.00698.2007 (2008).

    Article 

    Google Scholar
     

  • Vasylkiv, O. Y., Kubrak, O. I., Storey, K. B. & Lushchak, V. I. Catalase activity as a potential vital biomarker of fish intoxication by the herbicide aminotriazole. Pesticide Biochem. Physiol. 101, 1–5. https://doi.org/10.1016/j.pestbp.2011.05.005 (2011).

    Article 

    Google Scholar
     

  • Yasui, G. S. et al. Flow cytometric analysis from fish samples stored at low, ultra-low and cryogenic temperatures. Cryobiology 95, 68–71. https://doi.org/10.1016/j.cryobiol.2020.06.004 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A. & Moreno, E. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS. J. Chem. Theory Comput. 17, 6281–6291. https://doi.org/10.1021/acs.jctc.1c00645 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Rapin, N., Lund, O. & Castiglione, F. Immune system simulation online. Bioinformatics 27, 2013–2014. https://doi.org/10.1093/bioinformatics/btr335 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Grote, A. et al. JCat: A novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 33, W526–W531. https://doi.org/10.1093/nar/gki376 (2005).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Choi, S.-Y., Ro, H. & Yi, H. A prerequisite for cloning. In DNA Cloning: A Hands-On Approach (eds Choi, S.-Y. et al.) 5–28 (Springer, 2019).


    Google Scholar
     

  • Pizza, M. et al. Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science 287, 1816–1820. https://doi.org/10.1126/science.287.5459.1816 (2000).

    Article 
    PubMed 

    Google Scholar
     

  • Sette, A. & Rappuoli, R. Reverse vaccinology: Developing vaccines in the era of genomics. Immunity 33, 530–541. https://doi.org/10.1016/j.immuni.2010.09.017 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Srivastava, P. & Jain, K. C. Computer aided reverse vaccinology: A game-changer approach for vaccine development. Comb. Chem. High Through. Screen. 26, 1813–1821. https://doi.org/10.2174/1386207325666220930124013 (2023).

    Article 

    Google Scholar