HSPVdb—the Human Short Peptide Variation Database for improved mass spectrometry-based detection of polymorphic HLA-ligands

T cell epitopes derived from polymorphic proteins or from proteins encoded by alternative reading frames (ARFs) play an important role in (tumor) immunology. Identification of these peptides is successfully performed with mass spectrometry. In a mass spectrometry-based approach, the recorded tandem mass spectra are matched against hypothetical spectra generated from known protein sequence databases. Commonly used protein databases contain a minimal level of redundancy, and thus, are not suitable data sources for searching polymorphic T cell epitopes, either in normal or ARFs. At the same time, however, these databases contain much non-polymorphic sequence information, thereby complicating the matching of recorded and theoretical spectra, and increasing the potential for finding false positives. Therefore, we created a database with peptides from ARFs and peptide variation arising from single nucleotide polymorphisms (SNPs). It is based on the human mRNA sequences from the well-annotated reference sequence (RefSeq) database and associated variation information derived from the Single Nucleotide Polymorphism Database (dbSNP). In this process, we removed all non-polymorphic information. Investigation of the frequency of SNPs in the dbSNP revealed that many SNPs are non-polymorphic “SNPs”. Therefore, we removed those from our dedicated database, and this resulted in a comprehensive high quality database, which we coined the Human Short Peptide Variation Database (HSPVdb). The value of our HSPVdb is shown by identification of the majority of published polymorphic SNP- and/or ARF-derived epitopes from a mass spectrometry-based proteomics workflow, and by a large variety of polymorphic peptides identified as potential T cell epitopes in the HLA-ligandome presented by the Epstein–Barr virus cells

Class II MHC Self-Antigen Presentation in Human B and T Lymphocytes

Human CD4[superscript +] T cells process and present functional class II MHC-peptide complexes, but the endogenous peptide repertoire of these non-classical antigen presenting cells remains unknown. We eluted and sequenced HLA-DR-bound self-peptides presented by CD4[superscript +] T cells in order to compare the T cell-derived peptide repertoire to sequences derived from genetically identical B cells. We identified several novel epitopes derived from the T cell-specific proteome, including fragments of CD4 and IL-2. While these data confirm that T cells can present peptides derived from the T-cell specific proteome, the vast majority of peptides sequenced after elution from MHC were derived from the common proteome. From this pool, we identified several identical peptide epitopes in the T and B cell repertoire derived from common endogenous proteins as well as novel endogenous epitopes with promiscuous binding. These findings indicate that the endogenous HLA-DR-bound peptide repertoire, regardless of APC type and across MHC isotype, is largely derived from the same pool of self-protein.National Institutes of Health (U.S.) (grant P01AI039671)National Institutes of Health (U.S.) (P01AI045757

Concurrent Detection of Circulating Minor Histocompatibility Antigen-Specific CD8+ T Cells in SCT Recipients by Combinatorial Encoding MHC Multimers

Allogeneic stem cell transplantation (SCT) is a potentially curative treatment for patients with hematologic malignancies. Its therapeutic effect is largely dependent on recognition of minor histocompatibility antigens (MiHA) by donor-derived CD8+ T cells. Therefore, monitoring of multiple MiHA-specific CD8+ T cell responses may prove to be valuable for evaluating the efficacy of allogeneic SCT. In this study, we investigated the use of the combinatorial encoding MHC multimer technique to simultaneously detect MiHA-specific CD8+ T cells in peripheral blood of SCT recipients. Feasibility of this approach was demonstrated by applying dual-color encoding MHC multimers for a set of 10 known MiHA. Interestingly, single staining using a fluorochrome- and Qdot-based five-color combination showed comparable results to dual-color staining for most MiHA-specific CD8+ T cell responses. In addition, we determined the potential value of combinatorial encoding MHC multimers in MiHA identification. Therefore, a set of 75 candidate MiHA peptides was predicted from polymorphic genes with a hematopoietic expression profile and further selected for high and intermediate binding affinity for HLA-A2. Screening of a large cohort of SCT recipients resulted in the detection of dual-color encoded CD8+ T cells following MHC multimer-based T cell enrichment and short ex vivo expansion. Interestingly, candidate MiHA-specific CD8+ T cell responses for LAG3 and TLR10 derived polymorphic peptides could be confirmed by genotyping of the respective SNPs. These findings demonstrate the potency of the combinatorial MHC multimer approach in the monitoring of CD8+ T cell responses to known and potential MiHA in limited amounts of peripheral blood from allogeneic SCT recipients

T-Cell Epitope Prediction: Rescaling Can Mask Biological Variation between MHC Molecules

Theoretical methods for predicting CD8+ T-cell epitopes are an important tool in vaccine design and for enhancing our understanding of the cellular immune system. The most popular methods currently available produce binding affinity predictions across a range of MHC molecules. In comparing results between these MHC molecules, it is common practice to apply a normalization procedure known as rescaling, to correct for possible discrepancies between the allelic predictors. Using two of the most popular prediction software packages, NetCTL and NetMHC, we tested the hypothesis that rescaling removes genuine biological variation from the predicted affinities when comparing predictions across a number of MHC molecules. We found that removing the condition of rescaling improved the prediction software's performance both qualitatively, in terms of ranking epitopes, and quantitatively, in the accuracy of their binding affinity predictions. We suggest that there is biologically significant variation among class 1 MHC molecules and find that retention of this variation leads to significantly more accurate epitope prediction

Nitration of the Pollen Allergen Bet v 1.0101 Enhances the Presentation of Bet v 1-Derived Peptides by HLA-DR on Human Dendritic Cells

Nitration of pollen derived allergens can occur by NO2 and ozone in polluted air and it has already been shown that nitrated major birch (Betula verrucosa) pollen allergen Bet v 1.0101 (Bet v 1) exhibits an increased potency to trigger an immune response. However, the mechanisms by which nitration might contribute to the induction of allergy are still unknown. In this study, we assessed the effect of chemically induced nitration of Bet v 1 on the generation of HLA-DR associated peptides. Human dendritic cells were loaded with unmodified Bet v 1 or nitrated Bet v 1, and the naturally processed HLA-DR associated peptides were subsequently identified by liquid chromatography-mass spectrometry. Nitration of Bet v 1 resulted in enhanced presentation of allergen-derived HLA-DR-associated peptides. Both the copy number of Bet v 1 derived peptides as well as the number of nested clusters was increased. Our study shows that nitration of Bet v 1 alters antigen processing and presentation via HLA-DR, by enhancing both the quality and the quantity of the Bet v 1-specific peptide repertoire. These findings indicate that air pollution can contribute to allergic diseases and might also shed light on the analogous events concerning the nitration of self-proteins

Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

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

Inflammation-Associated Nitrotyrosination Affects TCR Recognition through Reduced Stability and Alteration of the Molecular Surface of the MHC Complex

Nitrotyrosination of proteins, a hallmark of inflammation, may result in the production of MHC-restricted neoantigens that can be recognized by T cells and bypass the constraints of immunological self-tolerance. Here we biochemically and structurally assessed how nitrotyrosination of the lymphocytic choriomeningitis virus (LCMV)-associated immunodominant MHC class I-restricted epitopes gp33 and gp34 alters T cell recognition in the context of both H-2Db and H-2Kb. Comparative analysis of the crystal structures of H-2Kb/gp34 and H-2Kb/NY-gp34 demonstrated that nitrotyrosination of p3Y in gp34 abrogates a hydrogen bond interaction formed with the H-2Kb residue E152. As a consequence the conformation of the TCR-interacting E152 was profoundly altered in H-2Kb/NY-gp34 when compared to H-2Kb/gp34, thereby modifying the surface of the nitrotyrosinated MHC complex. Furthermore, nitrotyrosination of gp34 resulted in structural over-packing, straining the overall conformation and considerably reducing the stability of the H-2Kb/NY-gp34 MHC complex when compared to H-2Kb/gp34. Our structural analysis also indicates that nitrotyrosination of the main TCR-interacting residue p4Y in gp33 abrogates recognition of H-2Db/gp33-NY complexes by H-2Db/gp33-specific T cells through sterical hindrance. In conclusion, this study provides the first structural and biochemical evidence for how MHC class I-restricted nitrotyrosinated neoantigens may enable viral escape and break immune tolerance

Evolutionarily Conserved Protein Sequences of Influenza A Viruses, Avian and Human, as Vaccine Targets

10.1371/journal.pone.0001190PLoS ONE21