Individual and population-level variability in HLA-DR associated immunogenicity risk of biologics used for the treatment of rheumatoid arthritis.

Hypothesis: While conventional in silico immunogenicity risk assessments focus on measuring immunogenicity based on the potential of therapeutic proteins to be processed and presented by a global population-wide set of human leukocyte antigen (HLA) alleles to T cells, future refinements might adjust for HLA allele frequencies in different geographic regions or populations, as well for as individuals in those populations. Adjustment by HLA allele distribution may reveal risk patterns that are specific to population groups or individuals, which current methods that rely on global-population HLA prevalence may obscure. Key findings: This analysis uses HLA frequency-weighted binding predictions to define immunogenicity risk for global and sub-global populations. A comparison of assessments tuned for North American/European versus Japanese/Asian populations suggests that the potential for anti-therapeutic responses (anti-therapeutic antibodies or ATA) for several commonly prescribed Rheumatoid Arthritis (RA) therapeutic biologics may differ, significantly, between the Caucasian and Japanese populations. This appears to align with reports of differing product-related immunogenicity that is observed in different populations. Relevance to clinical practice: Further definition of population-level (regional) and individual patient-specific immunogenic risk profiles may enable prescription of the RA therapeutic with the highest probability of success to each patient, depending on their population of origin and/or their individual HLA background. Furthermore, HLA-specific immunogenicity outcomes data are limited, thus there is a need to expand HLA-association studies that examine the relationship between HLA haplotype and ATA in the clinic.

In silico methods for immunogenicity risk assessment and human homology screening for therapeutic antibodies.

In silico immunogenicity risk assessment has been an important step in the development path for many biologic therapeutics, including monoclonal antibodies. Even if the source of a given biologic is 'fully human', T cell epitopes that are contained in the sequences of the biologic may activate the immune system, enabling the development of anti-drug antibodies that can reduce drug efficacy and may contribute to adverse events. Computational tools that identify T cell epitopes from primary amino acid sequences have been used to assess the immunogenic potential of therapeutic candidates for several decades. To facilitate larger scale analyses and accelerate preclinical immunogenicity risk assessment, our group developed an integrated web-based platform called ISPRI, (Immunogenicity Screening and Protein Re-engineering Interface) that provides hands-on access through a secure web-based interface for scientists working in large and mid-sized biotech companies in the US, Europe, and Japan. This toolkit has evolved and now contains an array of algorithms that can be used individually and/or consecutively for immunogenicity assessment and protein engineering. Most analyses start with the advanced epitope mapping tool (EpiMatrix), then proceed to identify epitope clusters using ClustiMer, and then use a tool called JanusMatrix to define whether any of the T cell epitope clusters may generate a regulatory T cell response which may diminish or eliminate anti-drug antibody formation. Candidates can be compared to similar products on a normalized immunogenicity scale. Should modifications to the biologic sequence be an option, a tool for moderating putative immunogenicity by editing T cell epitopes out of the sequence is available (OptiMatrix). Although this perspective discusses the in-silico immunogenicity risk assessment for monoclonal antibodies, bi-specifics, multi-specifics, and antibody-drug conjugates, the analysis of additional therapeutic modalities such as enzyme replacement proteins, blood factor proteins, CAR-T, gene therapy products, and peptide drugs is also made available on the ISPRI platform.

ISPRI (Interactive Screening and Protein Reengineering Interface): Integrated, cloud-based, comprehensive toolkit for Immunogenicity Risk Assessment.EpiMatrix Immunogenicity Score: Combined T effector and Treg Epitope Content per unit protein.Tregitopes: Treg Epitopes found in IgG Framework that have been shown to modulate antigen-specific effector T cell responses.ClustiMer: Tool for identifying epitope rich polypeptides from within a given protein sequence.JanusMatrix: Tool for Predicting Tolerance, Putative Treg Epitopes, and Anti-self-immune responses.OptiMatrix: Tool for modifying T cell epitope sequences to reduce (or enhance) MHC binding.

A SARS-CoV-2 NSP7 homolog of a Treg epitope suppresses CD4+ and CD8+ T cell memory responses.

Pathogens escape host defenses by T-cell epitope mutation or deletion (immune escape) and by simulating the appearance of human T cell epitopes (immune camouflage). We identified a highly conserved, human-like T cell epitope in non-structural protein 7 (NSP7) of SARS-CoV-2, RNA-dependent RNA polymerase (RdRp) hetero-tetramer complex. Remarkably, this T cell epitope has significant homology to a T regulatory cell epitope (Tregitope) previously identified in the Fc region of human immunoglobulin G (IgG) (Tregitope 289). We hypothesized that the SARS-CoV-2 NSP7 epitope (NSP7-289) may induce suppressive responses by engaging and activating pre-existing regulatory T cells. We therefore compared NSP7-289 and IgG Tregitopes (289 and 289z, a shorter version of 289 that isolates the shared NSP7 epitope) in vitro. Tregitope peptides 289, 289z and NSP7-289 bound to multiple HLA-DRB1 alleles in vitro and suppressed CD4+ and CD8+ T cell memory responses. Identification and in vitro validation of SARS-CoV-2 NSP7-289 provides further evidence of immune camouflage and suggests that pathogens can use human-like epitopes to evade immune response and potentially enhance host tolerance. Further exploration of the role of cross-conserved Tregs in human immune responses to pathogens such as SARS-CoV-2 is warranted.

Identification of immunodominant T cell epitopes induced by natural Zika virus infection.

Zika virus (ZIKV) is a flavivirus primarily transmitted by Aedes species mosquitoes, first discovered in Africa in 1947, that disseminated through Southeast Asia and the Pacific Islands in the 2000s. The first ZIKV infections in the Americas were identified in 2014, and infections exploded through populations in Brazil and other countries in 2015/16. ZIKV infection during pregnancy can cause severe brain and eye defects in offspring, and infection in adults has been associated with higher risks of Guillain-Barré syndrome. We initiated a study to describe the natural history of Zika (the disease) and the immune response to infection, for which some results have been reported. In this paper, we identify ZIKV-specific CD4+ and CD8+ T cell epitopes that induce responses during infection. Two screening approaches were utilized: an untargeted approach with overlapping peptide arrays spanning the entire viral genome, and a targeted approach utilizing peptides predicted to bind human MHC molecules. Immunoinformatic tools were used to identify conserved MHC class I supertype binders and promiscuous class II binding peptide clusters predicted to bind 9 common class II alleles. T cell responses were evaluated in overnight IFN-γ ELISPOT assays. We found that MHC supertype binding predictions outperformed the bulk overlapping peptide approach. Diverse CD4+ T cell responses were observed in most ZIKV-infected participants, while responses to CD8+ T cell epitopes were more limited. Most individuals developed a robust T cell response against epitopes restricted to a single MHC class I supertype and only a single or few CD8+ T cell epitopes overall, suggesting a strong immunodominance phenomenon. Noteworthy is that many epitopes were commonly immunodominant across persons expressing the same class I supertype. Nearly all of the identified epitopes are unique to ZIKV and are not present in Dengue viruses. Collectively, we identified 31 immunogenic peptides restricted by the 6 major class I supertypes and 27 promiscuous class II epitopes. These sequences are highly relevant for design of T cell-targeted ZIKV vaccines and monitoring T cell responses to Zika virus infection and vaccination.

H1N1 G4 swine influenza T cell epitope analysis in swine and human vaccines and circulating strains uncovers potential risk to swine and humans.

BACKGROUND: Pandemic influenza viruses may emerge from animal reservoirs and spread among humans in the absence of cross-reactive antibodies in the human population. Immune response to highly conserved T cell epitopes in vaccines may still reduce morbidity and limit the spread of the new virus even when cross-protective antibody responses are lacking. METHODS: We used an established epitope content prediction and comparison tool, Epitope Content Comparison (EpiCC), to assess the potential for emergent H1N1 G4 swine influenza A virus (G4) to impact swine and human populations. We identified and computed the total cross-conserved T cell epitope content in HA sequences of human seasonal and experimental influenza vaccines, swine influenza vaccines from Europe and the United States (US) against G4. RESULTS: The overall T cell epitope content of US commercial swine vaccines was poorly conserved with G4, with an average T cell epitope coverage of 35.7%. EpiCC scores for the comparison between current human influenza vaccines and circulating human influenza strains were also very low. In contrast, the T cell epitope coverage of a recent European swine influenza vaccine (HL03) was 65.8% against G4. CONCLUSIONS: Poor T cell epitope cross-conservation between emergent G4 and swine and human influenza vaccines in the US may enable G4 to spread in swine and spillover to human populations in the absence of protective antibody response. One European influenza vaccine, HL03, may protect against emergent G4. This study illustrates the use of the EpiCC tool for prospective assessment of existing vaccine strains against emergent viruses in swine and human populations.

In silico Immunogenicity Assessment for Sequences Containing Unnatural Amino Acids: A Method Using Existing in silico Algorithm Infrastructure and a Vision for Future Enhancements.

The in silico prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an in silico method for modeling the impact of a given UAA on a peptide's likelihood of binding to HLA and, by extension, its immunogenic potential. In silico assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory in vitro, ex vivo and in vivo assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating in silico immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and "correct" in silico estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.

Highly conserved, non-human-like, and cross-reactive SARS-CoV-2 T cell epitopes for COVID-19 vaccine design and validation.

Natural and vaccine-induced SARS-CoV-2 immunity in humans has been described but correlates of protection are not yet defined. T cells support the SARS-CoV-2 antibody response, clear virus-infected cells, and may be required to block transmission. In this study, we identified peptide epitopes associated with SARS-CoV-2 T-cell immunity. Using immunoinformatic methods, T-cell epitopes from spike, membrane, and envelope were selected for maximal HLA-binding potential, coverage of HLA diversity, coverage of circulating virus, and minimal potential cross-reactivity with self. Direct restimulation of PBMCs collected from SARS-CoV-2 convalescents confirmed 66% of predicted epitopes, whereas only 9% were confirmed in naive individuals. However, following a brief period of epitope-specific T-cell expansion, both cohorts demonstrated robust T-cell responses to 97% of epitopes. HLA-DR3 transgenic mouse immunization with peptides co-formulated with poly-ICLC generated a potent Th1-skewed, epitope-specific memory response, alleviating safety concerns of enhanced respiratory disease associated with Th2 induction. Taken together, these epitopes may be used to improve our understanding of natural and vaccine-induced immunity, and to facilitate the development of T-cell-targeted vaccines that harness pre-existing SARS-CoV-2 immunity.

New Immunoinformatics Tools for Swine: Designing Epitope-Driven Vaccines, Predicting Vaccine Efficacy, and Making Vaccines on Demand.

Novel computational tools for swine vaccine development can expand the range of immunization approaches available to prevent economically devastating swine diseases and spillover events between pigs and humans. PigMatrix and EpiCC are two new tools for swine T cell epitope identification and vaccine efficacy analysis that have been integrated into an existing computational vaccine design platform named iVAX. The iVAX platform is already in use for the development of human vaccines, thus integration of these tools into iVAX improves and expands the utility of the platform overall by making previously validated immunoinformatics tools, developed for humans, available for use in the design and analysis of swine vaccines. PigMatrix predicts T cell epitopes for a broad array of class I and class II swine leukocyte antigen (SLA) using matrices that enable the scoring of sequences for likelihood of binding to SLA. PigMatrix facilitates the prospective selection of T cell epitopes from the sequences of swine pathogens for vaccines and permits the comparison of those predicted epitopes with "self" (the swine proteome) and with sequences from other strains. Use of PigMatrix with additional tools in the iVAX toolkit also enables the computational design of vaccines in silico, for testing in vivo. EpiCC uses PigMatrix to analyze existing or proposed vaccines for their potential to protect, based on a comparison between T cell epitopes in the vaccine and circulating strains of the same pathogen. Performing an analysis of T cell epitope relatedness analysis using EpiCC may facilitate vaccine selection when a novel strain emerges in a herd and also permits analysis of evolutionary drift as a means of immune escape. This review of novel computational immunology tools for swine describes the application of PigMatrix and EpiCC in case studies, such as the design of cross-conserved T cell epitopes for swine influenza vaccine or for African Swine Fever. We also describe the application of EpiCC for determination of the best vaccine strains to use against circulating viral variants of swine influenza, swine rotavirus, and porcine circovirus type 2. The availability of these computational tools accelerates infectious disease research for swine and enable swine vaccine developers to strategically advance their vaccines to market.

The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS-CoV-2.

The outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas, or territories, causing staggering numbers of infections and deaths. In this study, bioinformatics analyses were performed on 5,568 complete genomes of SARS-CoV-2 virus to predict the T cell and B cell immunogenic epitopes of all viral proteins, which formed a systematic immune vulnerability landscape of SARS-CoV-2. The immune vulnerability and genetic variation profiles of SARS-CoV were compared with those of SARS-CoV and MERS-CoV. In addition, a web portal was developed to broadly share the data and results as a resource for the research community. Using this resource, we showed that genetic variations in SARS-CoV-2 are associated with loss of B cell immunogenicity, an increase in CD4+ T cell immunogenicity, and a minimum loss in CD8+ T cell immunogenicity, indicating the existence of a curious correlation between SARS-CoV-2 genetic evolutions and the immunity pressure from the host. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic/vaccine development and mechanistic research.

Better Epitope Discovery, Precision Immune Engineering, and Accelerated Vaccine Design Using Immunoinformatics Tools.

Computational vaccinology includes epitope mapping, antigen selection, and immunogen design using computational tools. Tools that facilitate the in silico prediction of immune response to biothreats, emerging infectious diseases, and cancers can accelerate the design of novel and next generation vaccines and their delivery to the clinic. Over the past 20 years, vaccinologists, bioinformatics experts, and advanced programmers based in Providence, Rhode Island, USA have advanced the development of an integrated toolkit for vaccine design called iVAX, that is secure and user-accessible by internet. This integrated set of immunoinformatic tools comprises algorithms for scoring and triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, re-engineering or eliminating regulatory T cell epitopes, and re-designing antigens to induce immunogenicity and protection against disease for humans and livestock. Commercial and academic applications of iVAX have included identifying immunogenic T cell epitopes in the development of a T-cell based human multi-epitope Q fever vaccine, designing novel influenza vaccines, identifying cross-conserved T cell epitopes for a malaria vaccine, and analyzing immune responses in clinical vaccine studies. Animal vaccine applications to date have included viral infections of pigs such as swine influenza A, PCV2, and African Swine Fever. "Rapid-Fire" applications for biodefense have included a demonstration project for Lassa Fever and Q fever. As recent infectious disease outbreaks underscore the significance of vaccine-driven preparedness, the integrated set of tools available on the iVAX toolkit stand ready to help vaccine developers deliver genome-derived, epitope-driven vaccines.

T cell epitope content comparison (EpiCC) analysis demonstrates a bivalent PCV2 vaccine has greater T cell epitope overlap with field strains than monovalent PCV2 vaccines.

Porcine circovirus type 2 (PCV2) has one of the highest evolutionary rates among DNA viruses. Traditionally, PCV2 vaccines have been based on the 2a genotype as this was the first genotype discovered. Today, eight genotypes of PCV2 viruses have been identified, and, taken together with the rapid evolutionary rate, propensity to recombine, and high rate of vaccination, further variation in PCV2 is expected. For these reasons, there is a growing genetic gap between available vaccines and field strains. When selecting vaccines, it is important to consider vaccines that contain T cell epitopes that are well-matched to the circulating strains. To quantify the relatedness between PCV2 vaccines and field strains, we predicted and compared their T cell epitope content and calculated Epitope Content Comparison (EpiCC) scores using established in silico tools. T cell epitopes predicted to bind common class I and class II swine leukocyte antigen (SLA) alleles were identified from two major structural proteins, the capsid (encoded by ORF2) and the replicase (encoded by ORF1). The T cell epitope content of three commercial PCV2a-based vaccines (a baculovirus expressed PCV2a ORF2 [VacAlt], a PCV1-PCV2a chimeric virus vaccine [VacA] and a combination cPCV2a-cPCV2b chimeric virus vaccine [VacAB]) and an experimental PCV2b ORF2-based chimeric virus vaccine [VacB] (Table 1), were compared to that of 161 PCV2 field strains (representing genotypes a-f). The T cell epitope content and conservation between vaccine and field strains varied. While all vaccine strains provided broad coverage of the field strains including heterologous genotypes, none of the vaccines covered all the putative T cell epitopes identified in the field strains. PCV2a-based vaccine strains generally scored higher in terms of conserved epitope content against PCV2a field isolates but were not identical. The PCV2b-based vaccine strain had higher scores against PCV2b and PCV2d field strains. The combination PCV2a-PCV2b vaccine (VacAB) had, on average, the highest EpiCC score. PCV2 continues to evolve and EpiCC analysis provides a new tool to assess the possible impact of virus genetic divergence on T cell epitope coverage of vaccine strains. Given that multiple genotypes are currently found and may co-exist on farms, this analysis suggests that a combination of PCV2a and PCV2b vaccine strains may be required to provide optimal coverage of current and future field isolates.

Immune escape and immune camouflage may reduce the efficacy of RTS,S vaccine in Malawi.

The RTS,S/AS01 malaria vaccine will undergo a pilot vaccination study in sub-Saharan Africa beginning in 2019. RTS,S/AS01 Phase III trials reported an efficacy of 28.3% (children 5-17 months) and 18.3% (infants 6-12 weeks), with substantial variability across study sites. We postulated that the relatively low efficacy of the RTS,S vaccine and variability across sites may be due to lack of T-cell epitopes in the vaccine antigen, and due to the HLA distribution of the vaccinated population, and/or due to 'immune camouflage', an immune escape mechanism. To examine these hypotheses, we used immunoinformatics tools to compare T helper epitopes contained in RTS,S vaccine antigens with Plasmodium falciparum circumsporozoite protein (CSP) variants isolated from infected individuals in Malawi. The prevalence of epitopes restricted by specific HLA-DRB1 alleles was inversely associated with prevalence of the HLA-DRB1 allele in the Malawi study population, suggesting immune escape. In addition, T-cell epitopes in the CSP of strains circulating in Malawi were more often restricted by low-frequency HLA-DRB1 alleles in the population. Furthermore, T-cell epitopes that were highly conserved across CSP variants in Malawi possessed TCR-facing residues that were highly conserved in the human proteome, potentially reducing T-cell help through tolerance. The CSP component of the RTS,S vaccine also exhibited a low degree of T-cell epitope relatedness to circulating variants. These results suggest that RTS,S vaccine efficacy may be impacted by low T-cell epitope content, reduced presentation of T-cell epitopes by prevalent HLA-DRB1, high potential for human-cross-reactivity, and limited conservation with the CSP of circulating malaria strains.

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.

In Vivo Validation of Predicted and Conserved T Cell Epitopes in a Swine Influenza Model.

Swine influenza is a highly contagious respiratory viral infection in pigs that is responsible for significant financial losses to pig farmers annually. Current measures to protect herds from infection include: inactivated whole-virus vaccines, subunit vaccines, and alpha replicon-based vaccines. As is true for influenza vaccines for humans, these strategies do not provide broad protection against the diverse strains of influenza A virus (IAV) currently circulating in U.S. swine. Improved approaches to developing swine influenza vaccines are needed. Here, we used immunoinformatics tools to identify class I and II T cell epitopes highly conserved in seven representative strains of IAV in U.S. swine and predicted to bind to Swine Leukocyte Antigen (SLA) alleles prevalent in commercial swine. Epitope-specific interferon-gamma (IFNγ) recall responses to pooled peptides and whole virus were detected in pigs immunized with multi-epitope plasmid DNA vaccines encoding strings of class I and II putative epitopes. In a retrospective analysis of the IFNγ responses to individual peptides compared to predictions specific to the SLA alleles of cohort pigs, we evaluated the predictive performance of PigMatrix and demonstrated its ability to distinguish non-immunogenic from immunogenic peptides and to identify promiscuous class II epitopes. Overall, this study confirms the capacity of PigMatrix to predict immunogenic T cell epitopes and demonstrate its potential for use in the design of epitope-driven vaccines for swine. Additional studies that match the SLA haplotype of animals with the study epitopes will be required to evaluate the degree of immune protection conferred by epitope-driven DNA vaccines in pigs.

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. CONCLUSION: 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).

H7N9 T-cell epitopes that mimic human sequences are less immunogenic and may induce Treg-mediated tolerance.

Avian-origin H7N9 influenza is a novel influenza A virus (IAV) that emerged in humans in China in 2013. Using immunoinformatics tools, we identified several H7N9 T cell epitopes with T cell receptor (TCR)-facing residues identical to those of multiple epitopes from human proteins. We hypothesized that host tolerance to these peptides may impair T helper response and contribute to the low titer, weak hemagglutination inhibiting (HI) antibody responses and diminished seroconversion rates that have been observed in human H7N9 infections and vaccine trials. We found that the magnitude of human T effector responses to individual H7N9 peptides was inversely correlated with the peptide's resemblance to self. Furthermore, a promiscuous T cell epitope from the hemagglutinin (HA) protein suppressed responses to other H7N9 peptides when co-administered in vitro. Along with other highly 'human-like' peptides from H7N9, this peptide was also shown to expand FoxP3(+) regulatory T cells (Tregs). Thus, H7N9 may be camouflaged from effective human immune response by T cell epitope sequences that avert or regulate effector T cell responses through host tolerance.

Immune camouflage: relevance to vaccines and human immunology.

High strain sequence variability, interference with innate immune mechanisms, and epitope deletion are all examples of strategies that pathogens have evolved to subvert host defenses. To this list we would add another strategy: immune camouflage. Pathogens whose epitope sequences are cross-conserved with multiple human proteins at the TCR-facing residues may be exploiting "ignorance and tolerance," which are mechanisms by which mature T cells avoid immune responses to self-antigens. By adopting amino acid configurations that may be recognized by autologous regulatory T cells, pathogens may be actively suppressing protective immunity. Using the new JanusMatrix TCR-homology-mapping tool, we have identified several such 'camouflaged' tolerizing epitopes that are present in the viral genomes of pathogens such as emerging H7N9 influenza. Thus in addition to the overall low number of T helper epitopes that is present in H7 hemaglutinin (as described previously, see http://dx.doi.org/10.4161/hv.24939), the presence of such tolerizing epitopes in H7N9 could explain why, in recent vaccine trials, whole H7N9-HA was poorly immunogenic and associated with low seroconversion rates (see http://dx.doi.org/10.4161/hv.28135). In this commentary, we provide an overview of the immunoinformatics process leading to the discovery of tolerizing epitopes in pathogen genomic sequences, provide a brief summary of laboratory data that validates the discovery, and point the way forward. Removal of viral, bacterial and parasite tolerizing epitopes may permit researchers to develop more effective vaccines and immunotherapeutics in the future.

Identification and retrospective validation of T-cell epitopes in the hepatitis C virus genotype 4 proteome: an accelerated approach toward epitope-driven vaccine development.

With over 150 million people chronically infected worldwide and millions more infected annually, hepatitis C continues to pose a burden on the global healthcare system. The standard therapy of hepatitis C remains expensive, with severe associated side effects and inconsistent cure rates. Vaccine development against the hepatitis C virus has been hampered by practical and biological challenges posed by viral evasion mechanisms. Despite these challenges, HCV vaccine research has presented a number of candidate vaccines that progressed to phase II trials. However, those efforts focused mainly on HCV genotypes 1 and 2 as vaccine targets and barely enough attention was given to genotype 4, the variant most prevalent in the Middle East and central Africa. We describe herein the in silico identification of highly conserved and immunogenic T-cell epitopes from the HCV genotype 4 proteome, using the iVAX immunoinformatics toolkit, as targets for an epitope-driven vaccine. We also describe a fast and inexpensive approach for results validation using the empirical data on the Immune Epitope Database (IEDB) as a reference. Our analysis identified 90 HLA class I epitopes of which 20 were found to be novel and 19 more had their binding predictions retrospectively validated; empirical data for the remaining 51 epitopes was insufficient to validate their binding predictions. Our analysis also identified 14 HLA class II epitopes, of which 8 had most of their binding predictions validated. Further investigation is required regarding the efficacy of the identified epitopes as vaccine targets in populations where HCV genotype 4 is most prevalent.

Smarter vaccine design will circumvent regulatory T cell-mediated evasion in chronic HIV and HCV infection.

Despite years of research, vaccines against HIV and HCV are not yet available, due largely to effective viral immunoevasive mechanisms. A novel escape mechanism observed in viruses that cause chronic infection is suppression of viral-specific effector CD4(+) and CD8(+) T cells by stimulating regulatory T cells (Tregs) educated on host sequences during tolerance induction. Viral class II MHC epitopes that share a T cell receptor (TCR)-face with host epitopes may activate Tregs capable of suppressing protective responses. We designed an immunoinformatic algorithm, JanusMatrix, to identify such epitopes and discovered that among human-host viruses, chronic viruses appear more human-like than viruses that cause acute infection. Furthermore, an HCV epitope that activates Tregs in chronically infected patients, but not clearers, shares a TCR-face with numerous human sequences. To boost weak CD4(+) T cell responses associated with persistent infection, vaccines for HIV and HCV must circumvent potential Treg activation that can handicap efficacy. Epitope-driven approaches to vaccine design that involve careful consideration of the T cell subsets primed during immunization will advance HIV and HCV vaccine development.

Integrated assessment of predicted MHC binding and cross-conservation with self reveals patterns of viral camouflage.

BACKGROUND: Immune recognition of foreign proteins by T cells hinges on the formation of a ternary complex sandwiching a constituent peptide of the protein between a major histocompatibility complex (MHC) molecule and a T cell receptor (TCR). Viruses have evolved means of "camouflaging" themselves, avoiding immune recognition by reducing the MHC and/or TCR binding of their constituent peptides. Computer-driven T cell epitope mapping tools have been used to evaluate the degree to which particular viruses have used this means of avoiding immune response, but most such analyses focus on MHC-facing 'agretopes'. Here we set out a new means of evaluating the TCR faces of viral peptides in addition to their agretopes, integrating evaluations of both sides of the ternary complex in a single analysis. METHODS: This paper develops what we call the Janus Immunogenicity Score (JIS), bringing together a well-established method for predicting MHC binding, with a novel assessment of the potential for TCR binding based on similarity with self. Intuitively, both good MHC binding and poor self-similarity are required for high immunogenicity (i.e., a robust T effector response). RESULTS: Focusing on the class II antigen-processing pathway, we show that the JIS of T effector epitopes and null or regulatory epitopes deposited in a large database of epitopes (Immune Epitope Database) are significantly different. We then show that different types of viruses display significantly different patterns of scores over their constituent peptides, with viruses causing chronic infection (Epstein-Barr and cytomegalovirus) strongly shifted to lower scores relative to those causing acute infection (Ebola and Marburg). Similarly we find distinct patterns among influenza proteins in H1N1 (a strain against which human populations rapidly developed immunity) and H5N1 and H7N9 (highly pathogenic avian flu strains, with significantly greater case mortality rates). CONCLUSION: The Janus Immunogenicity Score, which integrates MHC binding and TCR cross-reactivity, provides a new tool for studying immunogenicity of pathogens and may improve the selection and optimization of antigenic elements for vaccine design.