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Amino Acid Substitutions Analysis of the Putative Epitopes of Neuraminidase Protein from Influenza A/H1N1 Virus
Amino Acid Substitutions Analysis of the Putative Epitopes of Neuraminidase Protein from Influenza A H1N1 Virus
DOI:
10.46551/ruc.v22n1a04Palavras-chave:
Bioinformatics, Glycoprotein, Mutation, Neuraminidase, VaccineResumo
This study verified whether the neuraminidase protein of Influenza A H1N1 virus sequence has modified from 2009–2017 and its impact on the 2018 Brazilian vaccine. Method: The reference neuraminidase protein sequence from H1N1 Puerto Rico/1934 strain was subjected to three different methods of epitope prediction and the top five from each method were aligned using Clustal omega, resulting in eight putative epitopes. These epitopes were aligned to 7,438 neuraminidase sequences spanning from 2009–2017 and analyzed for specific amino acid substitutions and counted. The resultant neuraminidase protein was aligned against the 2015 and 2018 neuraminidase proteins, from Influenza A H1N1 virus subtypes, used for vaccine production. Result: Twenty-one main substitutions were detected, of which 16/21 (76.2%) substitutions points remained stable and 1/21 (4.8%) returned to the original amino acid residue in the viral population from 2009–2017. Additionally, 19% (4/21) substitutions occurred in Brazil and worldwide in this period, indicating that changes in the neuraminidase viral population profile is time-dependent rather than geographical. Conclusion: The neuraminidase protein containing these amino acid substitutions is more closely related to the neuraminidase protein from influenza A/Michigan/45/2015 than A/California/7/2009, supporting the replacement of this virus subtype in the Brazilian vaccine in 2018.
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Referências
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25 VITA, Randi et al. The Immune Epitope Database 2.0. Nucleic Acids Research, v. 38, n. suppl. 1, p. D854-D862, 2009.
26 BHASIN, Manoy; RAGHAVA, Gajendra P.S. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine, v. 22, n. 23, p. 3195–3204, 2004.
27 FOGED, Camilla; HANSEN, Jon; AGGER, Else M. License to kill: Formulation requirements for optimal priming of CD8 + CTL responses with particulate vaccine delivery systems. European Journal of Pharmaceutical Sciences, v. 45, n. 4, p. 482–491, 2012.
28 MELIDOU, Angeliki et al. Molecular and phylogenetic analysis of the haemagglutinin gene of pandemic influenza H1N1 2009 viruses associated with severe and fatal infections. Virus Research, v. 151, n. 2, p. 192–199, 2010.
29 ANDERSEN, Pernille H.; NIELSEN, Morten; LUND, Ole. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Science, v. 15, n. 11, p. 2558–2567, 2006.
30 WANG, Yulong et al. Determinants of antigenicity and specificity in immune response for protein sequences. BMC Bioinformatics, v. 12, n. 1, p. 251-263, 2011.
31 TRIER, N. H.; HANSEN, P. R.; HOUEN, G. 2012. Production and characterization of peptide antibodies. Methods, v. 56, n. 2, p. 136–144, 2012.
32 COX, N. J.; SUBBARAO, K. 2000. Global epidemiology of influenza: past and present. Annual Review of Medicine, v. 51, n. 1, p. 407–21, 2000.
33 SMITH, Derek J. et al. Mapping the antigenic and genetic evolution of influenza virus. Science, v. 305, n. 5682, p. 371–376, 2004.
2 PARADIS, Eric G. et al. Impact of the H275Y and I223V mutations in the neuraminidase of the 2009 pandemic influenza virus in vitro and evaluating experimental reproducibility. PLoS One, v. 10, n. 5; p. e0126115, 2015.
3 SHAO, Wenhan et al. Evolution of influenza a virus by mutation and re-assortment. International journal of molecular sciences, v. 18, n. 8, p. 1650-1662, 2017.
4 WU, Shuangsheng et al. Mortality burden from seasonal influenza and 2009 H1N1 pandemic influenza in Beijing, China, 2007-2013. Influenza and Other Respiratory Viruses, v. 12, n. 1, p. 88–97, 2018.
5 POPOVA, Anfisa V. et al. Allele-specific nonstationarity in evolution of influenza A virus surface proteins. Proceedings of the National Academy of Sciences, v. 116, n.42, p. 21104–21112, 2019.
6 ANDERSON, Christopher S. et al. Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation. BMC Bioinformatics, v. 19, n. 1, p. 51-61, 2018.
7 PETROVA, Velislava N.; RUSSELL, Colin A. The evolution of seasonal influenza viruses. Nature Review Microbiolology, v. 16, n. 1, p. 47–60, 2018.
8 BUSH, Robin M. Influenza Evolution. In: Encyclopedia of infectious diseases: Modern Methodologies. J Wiley & Sons, New York, USA, p. 199–214, 2007.
9 GOTTSCHALK, Alfred. Neuraminidase: the specific enzyme of influenza virus and Vibrio cholerae. BBA - Biochimica et Biophysica Acta, v. 23, n. C, p. 645–646, 1957.
10 ABED, Yacine et al. Impact of potential permissive neuraminidase mutations on viral fitness of the H275Y Oseltamivir-resistant Influenza A(H1N1)pdm09 virus in vitro, in mice and in ferrets. Journal of Virology, v. 88, n. 3, p. 1652–1658, 2014.
11 MOSCONA, Anne. Neuraminidase inhibitors for influenza. The New England Journal of Medicine, v. 353, n. 13, p. 1363–1373, 2005.
12 SUZUKI, Takashi et al. Sialidase activity of influenza A virus in an endocytic pathway enhances viral replication. Journal of Virology, v. 79, n.18, p. 11705–11715, 2005.
13 MATROSOVICH, Mikhail N. et al. Neuraminidase is important for the initiation of influenza virus infection in human airway epithelium. Journal of Virology, v. 78, n. 22, p. 12665–12667, 2004.
14 ABED, Yacine; BAZ, Mariana; BOIVIN, Guy. Impact of neuraminidase mutations conferring influenza resistance to neuraminidase inhibitors in the N1 and N2 genetic backgrounds. Antiviral Therapy, v. 11, n. 8, p. 971–976, 2006.
15 AOKI, Fred Y.; BOIVIN, Guy; ROBERTS, Noel. Influenza virus susceptibility and resistance to Oseltamivir. Antiviral Therapy, v. 12, n. 4 Pt B, p. 603–616, 2007.
16 BUTLER, Jeff et al. Estimating the fitness advantage conferred by permissive neuraminidase mutations in recent Oseltamivir-resistant A(H1N1)pdm09 Influenza Viruses. PLoS Pathogens, v. 10, n. 4, p. e1004065, 2014.
17 PETRIE, Joshua G. et al. Antibodies against the current influenza a (H1N1) vaccine strain do not protect some individuals from infection with contemporary circulating influenza A (H1N1) virus strains. Journal of Infectious Diseases, v. 214, n. 12, p. 1947–1951, 2016.
18 WORLD HEALTH ORGANIZATION – WHO. Recommended composition of influenza virus vaccines for use in the 2017–2018 northern hemisphere influenza season. Weekly Epidemiologycal Record, v. 92, n. 11, p. 117–128, 2017.
19 BRASIL. Ministério da Saúde. Resolução - RE No 2.696, de 6 de outubro de 2017. Aprova as vacinas influenza a serem comercializadas ou utilizadas no Brasil no ano de 2018. Diário Oficial da República Federativa do Brasil, v. 194, p. 76, 2017.
20 KELLEY, Lawrence A. et al. The Phyre2 web portal for protein modeling, prediction and analysis. Nature Protocols, v. 10, n. 6, p. 845-858, 2015.
21 QUAN, Lijun; LV, Qiang; ZHANG, Yang. STRUM: Structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics, v. 32, n. 19, p. 2936–2946, 2016.
22 LARSEN, Jens E.P.; LUND, Ole; NIELSEN, Morten. Improved method for predicting linear B-cell epitopes. Immunome Research, v. 2, n. 1, p. 2, 2006.
23 PARKER, J. M. R.; GUO, D.; HODGES, R. S. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and x-ray-derived accessible sites. Biochemistry, v. 25, n. 19, p. 5425–5432, 1986.
24 KOLASKAR, Ashok S.; TONGAONKAR, Prasad C. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Letters, v. 276, n. 1, p. 172–174, 1990.
25 VITA, Randi et al. The Immune Epitope Database 2.0. Nucleic Acids Research, v. 38, n. suppl. 1, p. D854-D862, 2009.
26 BHASIN, Manoy; RAGHAVA, Gajendra P.S. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine, v. 22, n. 23, p. 3195–3204, 2004.
27 FOGED, Camilla; HANSEN, Jon; AGGER, Else M. License to kill: Formulation requirements for optimal priming of CD8 + CTL responses with particulate vaccine delivery systems. European Journal of Pharmaceutical Sciences, v. 45, n. 4, p. 482–491, 2012.
28 MELIDOU, Angeliki et al. Molecular and phylogenetic analysis of the haemagglutinin gene of pandemic influenza H1N1 2009 viruses associated with severe and fatal infections. Virus Research, v. 151, n. 2, p. 192–199, 2010.
29 ANDERSEN, Pernille H.; NIELSEN, Morten; LUND, Ole. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Science, v. 15, n. 11, p. 2558–2567, 2006.
30 WANG, Yulong et al. Determinants of antigenicity and specificity in immune response for protein sequences. BMC Bioinformatics, v. 12, n. 1, p. 251-263, 2011.
31 TRIER, N. H.; HANSEN, P. R.; HOUEN, G. 2012. Production and characterization of peptide antibodies. Methods, v. 56, n. 2, p. 136–144, 2012.
32 COX, N. J.; SUBBARAO, K. 2000. Global epidemiology of influenza: past and present. Annual Review of Medicine, v. 51, n. 1, p. 407–21, 2000.
33 SMITH, Derek J. et al. Mapping the antigenic and genetic evolution of influenza virus. Science, v. 305, n. 5682, p. 371–376, 2004.
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Publicado
2020-09-06
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- 2021-01-26 (2)
- 2020-09-06 (1)
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LUDMILA ALVES DIAS SOUTO; ALESSANDRA REJANE ERICSSON DE OLIVEIRA XAVIER; APARECIDO DE SOUSA XAVIER, M. Amino Acid Substitutions Analysis of the Putative Epitopes of Neuraminidase Protein from Influenza A/H1N1 Virus: Amino Acid Substitutions Analysis of the Putative Epitopes of Neuraminidase Protein from Influenza A H1N1 Virus. Revista Unimontes Científica, [S. l.], v. 22, n. 1, p. 1–16, 2020. DOI: 10.46551/ruc.v22n1a04. Disponível em: https://www.periodicos.unimontes.br/index.php/unicientifica/article/view/2721. Acesso em: 21 nov. 2024.
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