Development and Validation of an Epitope Prediction Tool for Swine (PigMatrix) Based on the Pocket Profile Method

Background: T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the “pocket profile method”, we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences. We developed epitope-prediction matrices (PigMatrices), for three SLA class I alleles (SLA-1*0401, 2*0401 and 3*0401) and one class II allele (SLA-DRB1*0201), based on the binding preferences of the best-matched Human Leukocyte Antigen (HLA) pocket for each SLA pocket. The contact residues involved in the binding pockets were defined for class I based on crystal structures of either SLA (SLA-specific contacts, Ssc) or HLA supertype alleles (HLA contacts, Hc); for class II, only Hc was possible. Different substitution matrices were evaluated (PAM and BLOSUM) for scoring pocket similarity and identifying the best human match. The accuracy of the PigMatrices was compared to available online swine epitope prediction tools such as PickPocket and NetMHCpan. Results: PigMatrices that used Ssc to define the pocket sequences and PAM30 to score pocket similarity demonstrated the best predictive performance and were able to accurately separate binders from random peptides. For SLA-1*0401 and 2*0401, PigMatrix achieved area under the receiver operating characteristic curves (AUC) of 0.78 and 0.73, respectively, which were equivalent or better than PickPocket (0.76 and 0.54) and NetMHCpan version 2.4 (0.41 and 0.51) and version 2.8 (0.72 and 0.71). In addition, we developed the first predictive SLA class II matrix, obtaining an AUC of 0.73 for existing SLA-DRB1*0201 epitopes. Notably, PigMatrix achieved this level of predictive power without training on SLA binding data. Conclusions: Overall, the pocket profile method combined with binding preferences from HLA binding data shows significant promise for developing T cell epitope prediction tools for pigs. When combined with existing vaccine design algorithms, PigMatrix will be useful for developing genome-derived vaccines for a range of pig pathogens for which no effective vaccines currently exist (e.g. porcine reproductive and respiratory syndrome, influenza and porcine epidemic diarrhea)

A comparative analysis of SLA-DRB1 genetic diversity in Colombian (creoles and commercial line) and worldwide swine populations

Analysing pig class II mayor histocompatibility complex (MHC) molecules is mainly related to antigen presentation. Identifying frequently-occurring alleles in pig populations is an important aspect to be considered when developing peptide-based vaccines. Colombian creole pig populations have had to adapt to local conditions since entering Colombia; a recent census has shown low amounts of pigs which is why they are considered protected by the Colombian government. Commercial hybrids are more attractive regarding production. This research has been aimed at describing the allele distribution of Colombian pigs from diverse genetic backgrounds and comparing Colombian SLA-DRB1 locus diversity to that of internationally reported populations. Twenty SLA-DRB1 alleles were identified in the six populations analysed here using sequence-based typing. The amount of alleles ranged from six (Manta and Casco Mula) to nine (San Pedreño). Only one allele (01:02) having > 5% frequency was shared by all three commercial line populations. Allele 02:01:01 was shared by five populations (around > 5% frequency). Global FST indicated that pig populations were clearly structured, as 20.6% of total allele frequency variation was explained by differences between populations (FST = 0.206). This study’s results confirmed that the greatest diversity occurred in wild boars, thereby contrasting with low diversity in domestic pig populations.This work was supported by the Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A)

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

Background: Predicting vaccine efficacy against emerging pathogen strains is a significant problem in human and animal vaccine design. T‐cell epitope cross‐conservation may play an important role in cross‐strain vaccine efficacy. While influenza A virus (IAV) hemagglutination inhibition (HI) antibody titers are widely used to predict protective efficacy of 1 IAV vaccine against new strains, no similar correlate of protection has been identified for T‐cell epitopes. Objective: We developed a computational method (EpiCC) that facilitates pairwise comparison of protein sequences based on an immunological property—T‐cell epitope content—rather than sequence identity, and evaluated its ability to classify swine IAV strain relatedness to estimate cross‐protective potential of a vaccine strain for circulating viruses. Methods: T‐cell epitope relatedness scores were assessed for 23 IAV HA sequences representing the major H1 swine IAV phylo‐clusters circulating in North American swine and HA sequences in a commercial inactivated vaccine (FluSure XP®). Scores were compared to experimental data from previous efficacy studies. Results: Higher EpiCC scores were associated with greater protection by the vaccine against strains for 23 field IAV strain vaccine comparisons. A threshold for EpiCC relatedness associated with full or partial protection in the absence of cross‐reactive HI antibodies was identified. EpiCC scores for field strains for which FluSure protective efficacy is not yet available were also calculated. Conclusion: EpiCC thresholds can be evaluated for predictive accuracy of protection in future efficacy studies. EpiCC may also complement HI cross‐reactivity and phylogeny for selection of influenza strains in vaccine development

Swine influenza A virus: challenges and novel vaccine strategies

Swine Influenza A Virus (IAV-S) imposes a significant impact on the pork industry and has been deemed a significant threat to global public health due to its zoonotic potential. The most effective method of preventing IAV-S is vaccination. While there are tremendous efforts to control and prevent IAV-S in vulnerable swine populations, there are considerable challenges in developing a broadly protective vaccine against IAV-S. These challenges include the consistent diversification of IAV-S, increasing the strength and breadth of adaptive immune responses elicited by vaccination, interfering maternal antibody responses, and the induction of vaccine-associated enhanced respiratory disease after vaccination. Current vaccination strategies are often not updated frequently enough to address the continuously evolving nature of IAV-S, fail to induce broadly cross-reactive responses, are susceptible to interference, may enhance respiratory disease, and can be expensive to produce. Here, we review the challenges and current status of universal IAV-S vaccine research. We also detail the current standard of licensed vaccines and their limitations in the field. Finally, we review recently described novel vaccines and vaccine platforms that may improve upon current methods of IAV-S control

Additional file 3: of Development and validation of an epitope prediction tool for swine (PigMatrix) based on the pocket profile method

Contact residues in the SLA class II binding pockets based on HLA contacts (Hc)

Parasites and immunogenetic diversity in prairie dogs

2018 Summer.Includes bibliographical references.Prairie dogs (Cynomys spp.) are an important component of North American grassland communities. Prairie dogs have been characterized as ecosystem engineers and keystone species because their extensive burrow systems alter ecosystem dynamics and provide homes for a variety of species. Prairie dog populations have declined dramatically over the past century as a result of eradication programs, habitat loss, and introduced plague. This research explores factors related to host-parasite ecology and immunogenetics of prairie dogs. The second chapter is a systematic review of parasites recorded from all five prairie dog species. The third chapter characterizes genetic diversity and investigates selection at the hyperdiverse MHC DRB locus in black-tailed prairie dogs. The fourth chapter uses multimodel inference to investigate host and environmental factors affecting flea aggregation on black-tailed prairie dogs. I found host-parasite records documenting at least 104 parasites species from prairie dogs. Over 2/3 of parasite species were ectoparasites, primarily fleas. Most endoparasites were protozoa. Bacteria and viruses are essentially undescribed from prairie dogs. Potentially related to the diversity of parasites they are exposed to, the DRB gene in black-tailed prairie dogs was characterized by high levels of diversity. I also found considerable evidence for contemporary directional selection and historical balancing selection acting on the DRB gene in black-tailed prairie dogs. However, I found only weak evidence for a relationship between DRB genotype and flea parasitism in black-tailed prairie dogs. Primary drivers of flea aggregation on black-tailed prairie dogs, identified by multimodel inference of generalized linear models, were host sex and month of capture. Males were more heavily parasitized than females, and flea loads are greatest in September and lowest in June