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In silico design of recombinant multi-epitope vaccine against influenza A virus

BACKGROUND: Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine...

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Autores principales: Maleki, Avisa, Russo, Giulia, Parasiliti Palumbo, Giuseppe Alessandro, Pappalardo, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808469/
https://www.ncbi.nlm.nih.gov/pubmed/35109785
http://dx.doi.org/10.1186/s12859-022-04581-6
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author Maleki, Avisa
Russo, Giulia
Parasiliti Palumbo, Giuseppe Alessandro
Pappalardo, Francesco
author_facet Maleki, Avisa
Russo, Giulia
Parasiliti Palumbo, Giuseppe Alessandro
Pappalardo, Francesco
author_sort Maleki, Avisa
collection PubMed
description BACKGROUND: Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated. RESULTS: This study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a(+) vector. CONCLUSIONS: The proposed “Recombinant multi-epitope vaccine” was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04581-6.
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spelling pubmed-88084692022-02-02 In silico design of recombinant multi-epitope vaccine against influenza A virus Maleki, Avisa Russo, Giulia Parasiliti Palumbo, Giuseppe Alessandro Pappalardo, Francesco BMC Bioinformatics Research BACKGROUND: Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated. RESULTS: This study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a(+) vector. CONCLUSIONS: The proposed “Recombinant multi-epitope vaccine” was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04581-6. BioMed Central 2022-02-02 /pmc/articles/PMC8808469/ /pubmed/35109785 http://dx.doi.org/10.1186/s12859-022-04581-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Maleki, Avisa
Russo, Giulia
Parasiliti Palumbo, Giuseppe Alessandro
Pappalardo, Francesco
In silico design of recombinant multi-epitope vaccine against influenza A virus
title In silico design of recombinant multi-epitope vaccine against influenza A virus
title_full In silico design of recombinant multi-epitope vaccine against influenza A virus
title_fullStr In silico design of recombinant multi-epitope vaccine against influenza A virus
title_full_unstemmed In silico design of recombinant multi-epitope vaccine against influenza A virus
title_short In silico design of recombinant multi-epitope vaccine against influenza A virus
title_sort in silico design of recombinant multi-epitope vaccine against influenza a virus
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808469/
https://www.ncbi.nlm.nih.gov/pubmed/35109785
http://dx.doi.org/10.1186/s12859-022-04581-6
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