(436e) Predicting the Impact of Influenza A Virus Mutations on the Display of Viral Fragments to Helper T Cells

A virus can mutate its genetic code through either antigenic shift or antigenic drift, which can cause changes to the protein that the genes encode. Mutations in the protein canimprove functionality and survivability, which later variants maintain. Additionally, viral mutations can affect how a person’s immune system detects a mutated virus. Immune cells, such as T cells and antibodies, created during previous infections do not have the same detection capability when the virus protein is mutated. For this study, the change in immune recognition of helper T cells in humans was predicted for mutated Influenza A virus strains.

The 2009 Influenza A virus rapidly spread, causing the 2009 H1N1 pandemic. This virus is known as a zoonotic virus since it was transmitted from swine to humans. The primary Influenza proteins detected by the immune system are the surface proteins, Hemagglutinin (HA) and Neuraminidase (NA). Helper T cells detect small peptides of the virus that have been broken down and displayed on the surface of professional antigen-presenting cells (APC), such as macrophages. The peptides displayed to helper T cells are chosen based on their compatibility with the binding grooves of Major Histocompatibility Complex (MHC) class II molecules. Many different alleles encode these molecules, which have been passed down genetically. Each MHC Class II allele has different affinities for a variety of peptides. Thus, changes in the protein sequence can either improve or decline the presentation of peptides to the helper T cells. Vaccine development for influenza is difficult due to the mutation's speed and the different capabilities of detecting the virus within different ethnicities. Thus, getting a better insight into the effects of mutations in the Influenza virus on the MHC class II presentation to T cells could provide important insight.

The consensus sequences for every month during the 2009 pandemic needed to be identified for the two surface proteins. With the number of sequences available on the Influenza Virus Database provided by NCBI, the sequences were separated by month and submitted to ClustalOmega, a Multiple Sequence Alignment (MSA) software. With the sequences for each month aligned, a consensus sequence could be identified by designating each position with the highest percentage of amino acid for that position.

With access to the consensus sequences from April 2009 to October 2010, the change in helper T cell immune recognition could be calculated by comparing two consecutive sequences, with the first month as the wild type and the second month designated as the mutated sequence. Predicting the binding scores between the MHC class II molecules and the viral peptides can be done with ProPred, which can predict the binding scores of 51 alleles. The binding score between the wildtype and mutated sequences was subtracted and then divided over the highest possible binding score for that allele to understand the change in immune recognition.

While this does give the change in binding score, it does not provide a concise and meaningful result. Thus, a weighted score was determined using the allele frequencies for each ethnicity, obtained from the Allele Frequency Net Database, compared to the change in binding score for each allele. The products of each allele binding score and frequency were then added to create the weighted change in binding for each ethnicity.

After transitioning to humans, the 2009 Influenza A Virus showed increased immune recognition in the first couple of months. This is thought to be due to the virus improving its survivability. In the later part of the pandemic, the accumulated mutations decreased immune recognition, promoting immune evasion. When looking at the impacts on different ethnicities, the general trend was for all the weighted scores for the ethnicities to increase if the immune recognition increased, but at various amounts. This was the same for if the mutations caused a decrease in immune recognition. The consensus sequences for each month after the 2009 Influenza pandemic will be analyzed for further study. This will include investigating the different subtypes that have circulated the world, such as H3N2. It is also of interest to compare regional strains to those used to create the vaccine to understand the efficacy of designed vaccines.