Integrative Analysis of HTNV Glycoprotein Derived MHC II Epitopes by In Silico Prediction and Experimental Validation.
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ABSTRACT:
SUBMITTER: Sun H
PROVIDER: S-EPMC8326763 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Leishmaniasis is a significant worldwide health problem for which no vaccine exists. Activation of CD4(+) and CD8(+) T cells is crucial for the generation of protective immunity against parasite. Recent trend in vaccine design has been shifted to epitope-based vaccines that are more specific, safe, and easy to produce. In the present study, four known antigenic Leishmania infantum proteins, cysteine peptidase A (CPA), histone H1, KMP-11, and Leishmania eukaryotic initiation factor (LeIF) were analyzed for the prediction of binding epitopes to H2(d) MHC class I and II molecules, using online available algorithms. Based on in silico analysis, eight peptides including highly scored MHC class I- and II-restricted epitopes were synthesized. Peptide immunogenicity was validated in MHC compatible BALB/c mice immunized with each synthetic peptide emulsified in complete Freund's adjuvant/incomplete Freund's adjuvant. CPA_p2, CPA_p3, H1_p1, and LeIF_p6 induced strong spleen cell proliferation upon in vitro peptide re-stimulation. In addition, the majority of the peptides, except of LeIF_p1 and KMP-11_p1, induced IFN-γ secretion, while KMP-11_p1 indicated a suppressive effect on IL-10 production. CPA_p2, CPA_p3, LeIF_p3, and LeIF_p6 induced IFN-γ-producing CD4(+) T cells indicating a TH1-type response. In addition, CPA_p2, CPA_p3, and H1_p1 induced also the induction of CD8(+) T cells. The induction of peptide-specific IgG in immunized mice designated also the existence of B cell epitopes in peptide sequences. Combining immunoinformatic tools and experimental validation, we demonstrated that CPA_p2, CPA_p3, H1_p1, H1_p3, CPA_p2, LeIF_p3, and LeIF_p6 are likely to include potential epitopes for the induction of protective cytotoxic and/or TH1-type immune responses supporting the feasibility of peptide-based vaccine development for leishmaniasis.
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Project description:The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.
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Project description:mAbs to MHC class I (MHC-I) molecules have proved to be crucial reagents for tissue typing and fundamental studies of immune recognition. To augment our understanding of epitopic sites seen by a set of anti-MHC-I mAb, we determined X-ray crystal structures of four complexes of anti-MHC-I Fabs bound to peptide/MHC-I/β2-microglobulin (pMHC-I). An anti-H2-Dd mAb, two anti-MHC-I α3 domain mAbs, and an anti-β2-microglobulin mAb bind pMHC-I at sites consistent with earlier mutational and functional experiments, and the structures explain allelomorph specificity. Comparison of the experimentally determined structures with computationally derived models using AlphaFold Multimer showed that although predictions of the individual pMHC-I heterodimers were quite acceptable, the computational models failed to properly identify the docking sites of the mAb on pMHC-I. The experimental and predicted structures provide insight into strengths and weaknesses of purely computational approaches and suggest areas that merit additional attention.
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Project description:Monoclonal antibodies (mAb) to major histocompatibility complex class I (MHC-I) molecules have proved to be crucial reagents for tissue typing and fundamental studies of immune recognition. To augment our understanding of epitopic sites seen by a set of anti-MHC-I mAb, we determined X-ray crystal structures of four complexes of anti-MHC-I antigen-binding fragments (Fab) bound to peptide/MHC-I/β2m (pMHC-I). An anti-H2-Dd mAb, two anti-MHC-I α3 domain mAb, and an anti-β2-microglobulin (β2m) mAb bind pMHC-I at sites consistent with earlier mutational and functional experiments, and the structures explain allelomorph specificity. Comparison of the experimentally determined structures with computationally derived models using AlphaFold Multimer (AF-M) showed that although predictions of the individual pMHC-I heterodimers were quite acceptable, the computational models failed to properly identify the docking sites of the mAb on pMHC-I. The experimental and predicted structures provide insight into strengths and weaknesses of purely computational approaches and suggest areas that merit additional attention.
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Project description:BackgroundLeishmaniasis is caused by intracellular Leishmania parasites that induce a T-cell mediated response associated with recognition of CD4+ and CD8+ T cell Line 1Lineepitopes. Identification of CD8+ antigenic determinants is crucial for vaccine and therapy development. Herein, we developed an open-source software dedicated to search and compile data obtained from currently available on line prediction algorithms.Methodology/principal findingsWe developed a two-phase algorithm and implemented in an open source software called EPIBOT, that consolidates the results obtained with single prediction algorithms, generating a final output in which epitopes are ranked. EPIBOT was initially trained using a set of 831 known epitopes from 397 proteins from IEDB. We then screened 63 Leishmania braziliensis vaccine candidates with the EPIBOT trained tool to search for CD8+ T cell epitopes. A proof-of-concept experiment was conducted with the top eight CD8+ epitopes, elected by EPIBOT. To do this, the elected peptides were synthesized and validated for their in vivo cytotoxicity. Among the tested epitopes, three were able to induce lysis of pulsed-target cells.ConclusionOur results show that EPIBOT can successfully search across existing prediction tools, generating a compiled list of candidate CD8+ epitopes. This software is fast and a simple search engine that can be customized to search over different MHC alleles or HLA haplotypes.
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Project description:BackgroundCD (cluster of differentiation) 4+ T-cell responses to APOB (apolipoprotein B) are well characterized in atherosclerotic mice and detectable in humans. CD4+ T cells recognize antigenic peptides displayed on highly polymorphic HLA (human leukocyte antigen)-II. Immunogenicity of individual APOB peptides is largely unknown in humans. Only 1 HLA-II-restricted epitope was validated using the DRB1*07:01-APOB3036-3050 tetramer. We hypothesized that human APOB may contain discrete immunodominant CD4+ T-cell epitopes that trigger atherosclerosis-related autoimmune responses in donors with diverse HLA alleles.MethodsWe selected 20 APOB-derived peptides (APOB20) from an in silico screen and experimentally validated binding to the most commonly occurring human HLA-II alleles. We optimized a restimulation-based workflow to evaluate antigenicity of multiple candidate peptides in HLA-typed donors. This included activation-induced marker assay, intracellular cytokine staining, IFNγ (interferon gamma) enzyme-linked immunospot and cytometric bead array. High-throughput sequencing revealed TCR (T-cell receptor) clonalities of APOB-reactive CD4+ T cells.ResultsUsing stringent positive, negative, and crossover stimulation controls, we confirmed specificity of expansion-based protocols to detect CD4+ T cytokine responses to the APOB20 pool. Ex vivo assessment of AIM+CD4+ T cells revealed a statistically significant autoimmune response to APOB20 but not to a ubiquitously expressed negative control protein, actin. Resolution of CD4+ T responses to the level of individual peptides using IFNγ enzyme-linked immunospot led to the discovery of 6 immunodominant epitopes (APOB6) that triggered robust CD4+ T activation in most donors. APOB6-specific responding CD4+ T cells were enriched in unique expanded TCR clonotypes and preferentially expressed memory markers. Cytometric bead array analysis detected APOB6-induced secretion of both proinflammatory and regulatory cytokines. In clinical samples from patients with angiographically verified coronary artery disease, APOB6 stimulation induced higher activation and memory phenotypes and augmented secretion of proinflammatory cytokines TNF (tumor necrosis factor) and IFNγ, compared with patients with low coronary artery disease.ConclusionsUsing 3 cohorts, each with ≈20 donors, we discovered and validated 6 immunodominant, HLA-II-restricted APOB epitopes. The immune response to these APOB epitopes correlated with coronary artery disease severity.
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Project description:In the past decade, the substantial achievements of therapeutic cancer vaccines have shed a new light on cancer immunotherapy. The major challenge for designing potent therapeutic cancer vaccines is to identify neoantigens capable of inducing sufficient immune responses, especially involving major histocompatibility complex (MHC)-II epitopes. However, most previous studies on T-cell epitopes were focused on either ligand binding or antigen presentation by MHC rather than the immunogenicity of T-cell epitopes. In order to better facilitate a therapeutic vaccine design, in this study, we propose a revolutionary new tool: a convolutional neural network model named FIONA (Flexible Immunogenicity Optimization Neural-network Architecture) trained on IEDB datasets. FIONA could accurately predict the epitopes presented by the given specific MHC-II subtypes, as well as their immunogenicity. By leveraging the human leukocyte antigen allele hierarchical encoding model together with peptide dense embedding fusion encoding, FIONA (with AUC = 0.94) outperforms several other tools in predicting epitopes presented by MHC-II subtypes in head-to-head comparison; moreover, FIONA has unprecedentedly incorporated the capacity to predict the immunogenicity of epitopes with MHC-II subtype specificity. Therefore, we developed a reliable pipeline to effectively predict CD4+ T-cell immune responses against cancer and infectious diseases.
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Project description:Tumour-specific mutations are ideal targets for cancer immunotherapy as they lack expression in healthy tissues and can potentially be recognized as neo-antigens by the mature T-cell repertoire. Their systematic targeting by vaccine approaches, however, has been hampered by the fact that every patient's tumour possesses a unique set of mutations ('the mutanome') that must first be identified. Recently, we proposed a personalized immunotherapy approach to target the full spectrum of a patient's individual tumour-specific mutations. Here we show in three independent murine tumour models that a considerable fraction of non-synonymous cancer mutations is immunogenic and that, unexpectedly, the majority of the immunogenic mutanome is recognized by CD4(+) T cells. Vaccination with such CD4(+) immunogenic mutations confers strong antitumour activity. Encouraged by these findings, we established a process by which mutations identified by exome sequencing could be selected as vaccine targets solely through bioinformatic prioritization on the basis of their expression levels and major histocompatibility complex (MHC) class II-binding capacity for rapid production as synthetic poly-neo-epitope messenger RNA vaccines. We show that vaccination with such polytope mRNA vaccines induces potent tumour control and complete rejection of established aggressively growing tumours in mice. Moreover, we demonstrate that CD4(+) T cell neo-epitope vaccination reshapes the tumour microenvironment and induces cytotoxic T lymphocyte responses against an independent immunodominant antigen in mice, indicating orchestration of antigen spread. Finally, we demonstrate an abundance of mutations predicted to bind to MHC class II in human cancers as well by employing the same predictive algorithm on corresponding human cancer types. Thus, the tailored immunotherapy approach introduced here may be regarded as a universally applicable blueprint for comprehensive exploitation of the substantial neo-epitope target repertoire of cancers, enabling the effective targeting of every patient's tumour with vaccines produced 'just in time'.
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Project description:BackgroundThe Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool.ResultsWe evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively).ConclusionThe optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively.
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Project description:Ebolavirus causes highly lethal hemorrhagic fever in humans. The envelope-displayed viral glycoprotein (GP) is the primary target of humoral immunity induced by natural exposure and vaccination. No T cell epitopes in the GP have been characterized in humans. A phase I clinical trial of a heterologous prime-boost vaccination regime with viral vectors encoding filovirus antigens elicits humoral and T cell responses in vaccinees. The most frequently recognized peptide pools are deconvoluted to identify the minimal epitopes recognized by antigen-specific T cells. We characterize nine immunogenic epitopes on the Ebolavirus GP. Histocompatibility leukocyte antigen (HLA) typing with in silico epitope analysis determines the likely MHC class I restriction elements. Thirteen HLA-A and -B alleles are predicted to present the identified CD8+ T cell epitopes, suggesting promiscuous recognition and a broad immune response. Delivery of the Ebolavirus GP antigen by using a heterologous prime-boost approach is immunogenic in genetically diverse human populations, with responses against multiple epitopes.
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