Bioinformatical design of a vaccine against influenza virus N1 subtype Bonaccorsi, Irene; Clausen, Martin Bau; Høj, Leif Howalt; Kjær, Jesper and Sayyad, Fhayaz Ahammad Introduction Morbidity and mortality of influenza 5-20% of all people in the US are infected with influenza every year, 200,000 are hospitalised and 36,000 die [1]. This makes flu a serious pathogen that causes massive losses in productivity in the industrialised world and even large number of deaths. Influenza virus Human influenza viruses are members of the orthomyxovirus family, which consists of: influenza A, B, and C vira, and Thogovirus (in ticks). In humans, only influenza A and B viruses are of epidemiological interest. The main antigenic determinants of influenza A and B viruses are the hemagglutinin (HA or N) and neuraminidase (NA or N) transmembrane glycoproteins. Based on the antigenicity of these glycoproteins, influenza A viruses are further subdivided into sixteen H (H1-H16) and nine N (N1-N9) subtypes. Neuraminidase Like HA, neuraminidase is a glycoprotein, which is also found as projections on the surface of the virus where it forms a tetrameric structure. The NA molecule presents its main part at the outer surface of the cell, spans the lipid layer, and has a small cytoplasmic tail. NA acts as an enzyme, cleaving sialic acid from the HA molecule, from other NA molecules and from glycoproteins and glycolipids at the cell surface. It also serves as an important antigenic site, and in addition, seems to be necessary for the penetration of the virus through the mucin layer of the respiratory epithelium. Antigenic drift can also occur in the NA. The NA carries several important amino acid residues which, if they mutate, can lead to resistance against neuraminidase inhibitors. Targeting a distinct protein as vaccine candidate Choosing the protein Sequences of the human influenza A (covering the period ) were taken from the NCBI Influenza Virus Resource database [2]. A multiple alignment with ClustalW [3] of the NA subtype N1 revealed it to be the most conserved gene (see NJ tree in figure 1). A consensus sequence based on 197 protein sequences was then chosen to be our vaccine candidate. Neighbour-joining phylogenetic trees were then built using MEGA 3.1 [4], in order to ascertain the degree of conservation of NA in different influenza subtypes. Analysing the protein Class I MHC epitopes for all supertypes were found using NetCTL [5]. Epitopes for HLA-DR4 (an MHC II allele) were found using EasyGibbs [6]. A B-cell epitope was identified using BepiPred [7] using an HMM model and provides the residue scores as log odds. Results T-Cell epitopes Listed in the table 1 are all NetCTL prediction of good MHC-I epitope candidates in NA. Table 2 shows MHC-II epitopes. All of these are in highly conserved regions of NA sequences covering 2000 to B-Cell epitopes With BepiPred we predicted the region 322 to 348 to be the best region for a linear B-cell epitope. FGDNPRPKDGEGSCNPVTVDGANGVKG The region in bold is the best scoring 9mer epitope of the region (BepiPred score for all 9 residues: ). Figure 3 and 4 show 3D visualisations and figure 5 a logo of the epitope. Discussion We limited our work to influenza A virus and subtype N1 as we believe vaccines for influenza will be most efficient when targeted against a single subtype rather then multiple subtypes. The purpose of this approach is to keep the epitopes specific rather than general in order to achieve a focused immune response. The approach we made here can easily be repeated for other subtypes. We are aware that immune escape does happen. Any vaccine against influenza will have to be updated often and may also further be different based on specific regions in the world [11]. Question remains whether the approach here will be easier than the current vaccines based on heat-killed strains. The vaccines we have designed are based on a dataset for the period 2000 to As can be seen in figure 1, the N1 subtype sequences show a very high degree of similarity. This does indicate that epitope based vaccines may be useful for longer periods than the current methods which are updated every year [11]. The bioinformatical designed vaccines are in no way final, in vitro and in vivo tests are necessary to determine the real effect of the vaccines. The benefit of the bioinformatical approach is that the preliminary design is very cost efficient compared to standard laboratory trial and error approach to vaccine designs. Conclusion We have used bioinformatical tools to identify MHC-I and II supertype T- and B-cell epitope candidates in the highly conserved NA protein. In addition we constructed a polytope of highly conserved epitopes found in the full genome of the human Influenza A virus subtype N1. The methods used can easily be applied to other subtypes. Virus subtypes should be dealt with individually in order to make the vaccines specific and effective. References Creating a plasmid vaccine Sequences for all influenza proteins were taken from NCBI Influenza Virus Resource database [2] (only genes sequenced in the period were used). Multiple alignments were made using ClustalW [3], giving consensus sequences for all proteins. Using NetCTL [5] and EasyGibbs [6], respectively, epitopes for all MHC-I supertypes and HLA-DR4 were found. Polytope of all epitopes were constructed with triple A linker regions binding the epitopes together. The polytope was optimised with a Monte Carlo Metropolis simulation (MCM) implemented in polytope_cont3 (unpublished). MCM settings: all standard but with 700 iterations and 14 temperature steps. The final polytope was evaluated with NetCTL to check C-terminal cleavage, TAP translocation and affinity Results For the final polytope we replaced two epitopes (HLA: A3 and B44) with suboptimal epitopes (both ranked 2 nd in the NetCTL prediction) because these had better C-terminal cleavage than the best predicted. All epitopes were validated to be in highly conserved regions of the genes. polytope_cont3 only predicted three C-terminal cleavages within epitopes (HLA: B7, A3 and B58) and no additional epitopes would arise from the polytope. Given the stochastic nature of the proteasome we do not consider these few internal cleavage sites to be a problem. Figure 2. Neighbour Joining tree of 222 NA sequences (N1 subtype). The 25 H5N1 sequences forming the distinct lower clade were removed from the dataset before we generated the consensus sequence. Figure 1. Structure of an influenza A virus. Polymerase B1, B2 and A proteins (PB1 + PB2 + PA), Hemagglutinin (HA), Nucleocapsid protein (NP), Neuraminidase (NA), Matrix proteins (M1 + M2), Non-structural protein (NS) Figures: Figure 1. Image copyright by Dr. Markus Eickmann, Institute for Virology, Marburg, Germany. Litterature, webresources and tools: [1] CDC - Influenza (Flu) | What Everyone Should Know About Flu and the Flu Vaccine ( [2] NCBI Influenza Virus Resource ( [3] ClustalW: Thompson, J.D., Higgins, D.G. and Gibson, T.J. (1994). CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Research, submitted, June [4] Mega 3, Version 3.1. S Kumar, K Tamura, and M Nei (2004) MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment. Briefings in Bioinformatics 5: [5] An integrative approach to CTL epitope prediction. A combined algorithm integrating MHC-I binding, TAP transport efficiency, and proteasomal cleavage predictions. Larsen M.V., Lundegaard C., Kasper Lamberth, Buus S,. Brunak S., Lund O., and Nielsen M. European Journal of Immunology. 35(8): ( [6] Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S, Lund O. Bioinformatics : ( [7] Improved method for predicting linear B-cell epitopes Jens Erik Pontoppidan Larsen, Ole Lund and Morten Nielsen Immunome Research 2:2, ( [8] PyMOL version 0.99 ( [9] Crooks GE, Hon G, Chandonia JM, Brenner SE WebLogo: A sequence logo generator, Genome Research, 14: , (2004) ( [10] Rammensee, Friede, Stevanovic, MHC ligands and peptide motifs: 1st listing, Immunogenetics 41, , 1995 ( [11] The Influenza Sequence Database ( Table 2 Three highest scoring epitopes for HLA-DR4 MHC class-II. Predictions are made with EasyGibbs [6]. All identified epitopes are in highly conserved regions of NA. Are these epitopes novel? We searched for all identified epitopes in the SYFPEITHI database [10] and found no matches, however in general SYFPEITHI seems to be lacking epitopes for influenza. Most vaccines against influenza are based on heat- killed influenza strains and not epitopes. Table 1 Shown below are the best scoring predictions of epitopes in NA subtype N1 obtained with NetCTL. Predictions are made for MHC-1 binding (Affinity column), proteasome cleavage (C-terminal cleavage column) and TAP translocation. All 3 measures are combined to a single score (combined score) in which the listed epitopes were the highest scoring for the respective HLA supertypes. Epitope location column lists the position of the first amino acid in epitopes placement in the NA protein sequence Table 3 Shown below is the polytope (top row to bottom row of the epitope column). Further shown for each epitope is the location in gene and relative position (Epitope location), HLA supertype for the epitope and the validated rank of the epitope in NetCTL. DR4 is an MHC-II allele and thus not validated against NetCTL. Figure 4 Zoom of the predicted epitope area (red) highest scoring 9mer is coloured yellow (both figure 3 and 4 were generated in PyMOL [8] with 1V0Z.pdb) Figure 3 Tetramer structure of neuraminidase viewed from above (white lines show the 4 proteins). Non- yellow coloured residues are part of highly conserved sequence regions Figure 5 Logo [9] of the 9mer B-Cell epitope from figure 4 (across all 197 sequences). All positions are highly conserved although position 5 and 8 indicate presence of mutation and risk of immune escape.