Skip to main content
ARS Home » Midwest Area » Ames, Iowa » National Animal Disease Center » Food Safety and Enteric Pathogens Research » Research » Publications at this Location » Publication #330311

Research Project: Intestinal Microbial Ecology and Metagenomic Strategies to Reduce Antibiotic Resistance and Foodborne Pathogens

Location: Food Safety and Enteric Pathogens Research

Title: T cell epitope content comparison (EpiCC) of swine H1 influenza A virus hemagglutinin

item GUTIERREZ, ANDRES - University Of Rhode Island
item Loving, Crystal
item TERRY, FRANCES - Epivax, Inc
item MOISE, LEONARD - University Of Rhode Island
item MARTIN, WILLIAM - Epivax, Inc
item DE GROOT, ANNE - University Of Rhode Island

Submitted to: Influenza and Other Respiratory Viruses
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/4/2017
Publication Date: 11/28/2017
Citation: Gutierrez, A.H., Rapp-Gabrielson, V., Loving, C.L., Terry, F.E., Moise, L., Martin, W.D., De Groot, A.S. 2017. T cell epitope content comparison (EpiCC) of swine H1 influenza A virus hemagglutinin. Influenza and Other Respiratory Viruses. 11(6):531-542.

Interpretive Summary: Swine influenza is a problem for swine producers because of the large number of different strains currently present in the US swine population. While some portions of the virus are different, there are some portions that are the same across a number of different strains. However, it is important to evaluate portions of the virus that are seen by the immune system to maximize likelihood of protection from a commercial vaccine. In addition, using a vaccine that includes portions of the virus that a specific immune cell recognizes could provide a baseline level of protection against numerous strains of influenza. Our work shows that the bioinformatics tools developed (Epitope Content Comparison – EpiCC) can be used to evaluate relatedness of vaccine virus and field virus as it relates to the pig immune system. And relatedness at the immune levels was associated with protection in the absence of being able to detect other influenza-specific immune responses. Overall, EpiCC evaluation of influenza viruses can complement other tools used to identify the best commercial vaccine to use for protection against field strains of influenza virus. Collectively, the information reported is important for generation of swine influenza virus vaccines, as well as vaccines for other diseases of swine.

Technical Abstract: Background: The hemagglutinin (HA) is the major target of protective antibody responses to influenza A viruses (IAV); hemagglutination inhibition (HI) antibody titers are widely used to predict vaccine cross-protection. Objectives: To explore the possible role of T cell epitopes in vaccine-induced protection, we developed a method for epitope content comparison (EpiCC) and used it to compare H1 swine IAV. Methods: Twenty-three HA sequences from H1 swine IAV were screened using PigMatrix, a T cell epitope mapping algorithm that identifies 9mers that have the potential to bind specific class I and II swine leukocyte antigens. We then assessed HA T cell epitope content and epitope-based relatedness (EpiCC scores) between vaccine and field viruses. Experimental data from previous vaccine efficacy studies were summarized in the context of epitope content relatedness of the vaccine and challenge viruses. Results: T cell epitope content of the H1 sequences was different among viruses. Class I epitope content was lower than class II epitope content. Comparing the T cell epitope content of the gamma-cluster H1 vaccine virus to viruses for which data from previous experimental challenges were available, a relatedness threshold was established that could help explain cross-protection in the absence of HI cross-reactivity. Conclusion: EpiCC is a novel approach that uses predicted T cell epitope content to estimate the relatedness of genetically and antigenically variable viruses. Applying EpiCC to identify viruses that share high T cell epitope content could complement HI cross-reactivity and HA gene sequence data for selection of IAV vaccine strains and prediction of cross-protection. Keywords: Computational immunology, hemagglutinin, SLA, swine influenza H1 viruses, vaccine efficacy, T cell epitope content comparison, T cell epitope prediction.