Immunological Interaction of HLA-DPB1 and Proteinase 3 in ANCA Vasculitis is Associated with Clinical Disease Activity.

BACKGROUND: PR3-ANCA vasculitis has a genetic association with HLA-DPB1. We explored immunologic and clinical features related to the interaction of HLA-DPB1*04:01 with a strongly binding PR3 peptide epitope (PR3225-239). METHODS: Patients with ANCA vasculitis with active disease and disease in remission were followed longitudinally. Peripheral blood mononuclear cells from patients and healthy controls with HLA-DPB1*04:01 were tested for HLA-DPB1*04:01 expression and interaction with a PR3 peptide identified via in silico and in vitro assays. Tetramers (HLA/peptide multimers) identified autoreactive T cells in vitro. RESULTS: The HLA-DPB1*04:01 genotype was associated with risk of relapse in PR3-ANCA (HR for relapse 2.06; 95% CI, 1.01 to 4.20) but not in myeloperoxidase (MPO)-ANCA or the combined cohort. In silico predictions of HLA and PR3 peptide interactions demonstrated strong affinity between ATRLFPDFFTRVALY (PR3225-239) and HLA-DPB1*04:01 that was confirmed by in vitro competitive binding studies. The interaction was tested in ex vivo flow cytometry studies of labeled peptide and HLA-DPB1*04:01-expressing cells. We demonstrated PR3225-239 specific autoreactive T cells using synthetic HLA multimers (tetramers). Patients in long-term remission off therapy had autoantigenic peptide and HLA interaction comparable to that of healthy volunteers. CONCLUSIONS: The risk allele HLA-DPB1*04:01 has been associated with PR3-ANCA, but its immunopathologic role was unclear. These studies demonstrate that HLA-DPB1*04:01 and PR3225-239 initiate an immune response. Autoreactive T cells specifically recognized PR3225-239 presented by HLA-DPB1*04:01. Although larger studies should validate these findings, the pathobiology may explain the observed increased risk of relapse in our cohort. Moreover, lack of HLA and autoantigen interaction observed during long-term remission signals immunologic nonresponsiveness.

Restricted myeloperoxidase epitopes drive the adaptive immune response in MPO-ANCA vasculitis.

BACKGROUND: Treatment of autoimmune diseases has relied on broad immunosuppression. Knowledge of specific interactions between human leukocyte antigen (HLA), the autoantigen, and effector immune cells, provides the foundation for antigen-specific therapies. These studies investigated the role of HLA, specific myeloperoxidase (MPO) epitopes, CD4+ T cells, and ANCA specificity in shaping the immune response in patients with anti-neutrophil cytoplasmic autoantibody (ANCA) vasculitis. METHODS: HLA sequence-based typing identified enriched alleles in our patient population (HLA-DPB1*04:01 and HLA-DRB4*01:01), while in silico and in vitro binding studies confirmed binding between HLA and specific MPO epitopes. Class II tetramers with MPO peptides were utilized to detect autoreactive CD4+ T cells. TCR sequencing was performed to determine the clonality of T cell populations. Longitudinal peptide ELISAs assessed the temporal nature of anti-MPO447-461 antibodies. Solvent accessibility combined with chemical modification determined the buried regions of MPO. RESULTS: We identified a restricted region of MPO that was recognized by both CD4+ T cells and ANCA. The autoreactive T cell population contained CD4+CD25intermediateCD45RO+ memory T cells and secreted IL-17A. T cell receptor (TCR) sequencing demonstrated that autoreactive CD4+ T cells had significantly less TCR diversity when compared to naïve and memory T cells, indicating clonal expansion. The anti-MPO447-461 autoantibody response was detectable at onset of disease in some patients and correlated with disease activity in others. This region of MPO that is targeted by both T cells and antibodies is not accessible to solvent or chemical modification, indicating these epitopes are buried. CONCLUSIONS: These observations reveal interactions between restricted MPO epitopes and the adaptive immune system within ANCA vasculitis that may inform new antigen-specific therapies in autoimmune disease while providing insight into immunopathogenesis.

The oncoprotein TBX3 is controlling severity in experimental arthritis.

BACKGROUND: Development of autoimmune diseases is the result of a complex interplay between hereditary and environmental factors, with multiple genes contributing to the pathogenesis in human disease and in experimental models for disease. The T-box protein 3 is a transcriptional repressor essential during early embryonic development, in the formation of bone and additional organ systems, and in tumorigenesis. METHODS: With the aim to find novel genes important for autoimmune inflammation, we have performed genetic studies of collagen-induced arthritis (CIA), a mouse experimental model for rheumatoid arthritis. RESULTS: We showed that a small genetic fragment on mouse chromosome 5, including Tbx3 and three additional protein-coding genes, is linked to severe arthritis and high titers of anti-collagen antibodies. Gene expression studies have revealed differential expression of Tbx3 in B cells, where low expression was accompanied by a higher B cell response upon B cell receptor stimulation in vitro. Furthermore, we showed that serum TBX3 levels rise concomitantly with increasing severity of CIA. CONCLUSIONS: From these results, we suggest that TBX3 is a novel factor important for the regulation of gene transcription in the immune system and that genetic polymorphisms, resulting in lower expression of Tbx3, are contributing to a more severe form of CIA and high titers of autoantibodies. We also propose TBX3 as a putative diagnostic biomarker for rheumatoid arthritis.

Determination of a Predictive Cleavage Motif for Eluted Major Histocompatibility Complex Class II Ligands.

CD4+ T cells have a major role in regulating immune responses. They are activated by recognition of peptides mostly generated from exogenous antigens through the major histocompatibility complex (MHC) class II pathway. Identification of epitopes is important and computational prediction of epitopes is used widely to save time and resources. Although there are algorithms to predict binding affinity of peptides to MHC II molecules, no accurate methods exist to predict which ligands are generated as a result of natural antigen processing. We utilized a dataset of around 14,000 naturally processed ligands identified by mass spectrometry of peptides eluted from MHC class II expressing cells to investigate the existence of sequence signatures potentially related to the cleavage mechanisms that liberate the presented peptides from their source antigens. This analysis revealed preferred amino acids surrounding both N- and C-terminuses of ligands, indicating sequence-specific cleavage preferences. We used these cleavage motifs to develop a method for predicting naturally processed MHC II ligands, and validated that it had predictive power to identify ligands from independent studies. We further confirmed that prediction of ligands based on cleavage motifs could be combined with predictions of MHC binding, and that the combined prediction had superior performance. However, when attempting to predict CD4+ T cell epitopes, either alone or in combination with MHC binding predictions, predictions based on the cleavage motifs did not show predictive power. Given that peptides identified as epitopes based on CD4+ T cell reactivity typically do not have well-defined termini, it is possible that motifs are present but outside of the mapped epitope. Our attempts to take that into account computationally did not show any sign of an increased presence of cleavage motifs around well-characterized CD4+ T cell epitopes. While it is possible that our attempts to translate the cleavage motifs in MHC II ligand elution data into T cell epitope predictions were suboptimal, other possible explanations are that the cleavage signal is too diluted to be detected, or that elution data are enriched for ligands generated through an antigen processing and presentation pathway that is less frequently utilized for T cell epitopes.

Predicting HLA CD4 Immunogenicity in Human Populations.

BACKGROUND: Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides. METHODS: Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an "immunogenicity score." We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level. RESULTS: The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore). CONCLUSION: The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ.

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide-MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0 .

Bioinformatics identification of antigenic peptide: predicting the specificity of major MHC class I and II pathway players.

Bioinformatics methods for immunology have become increasingly used over the last decade and now form an integrated part of most epitope discovery projects. This wide usage has led to the confusion of defining which of the many methods to use for what problems. In this chapter, an overview is given focusing on the suite of tools developed at the Technical University of Denmark.

NetMHCcons: a consensus method for the major histocompatibility complex class I predictions.

A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC-peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple combination of NetMHC and NetMHCpan gives the highest performance when the allele in question is included in the training and is characterized by at least 50 data points with at least ten binders. Otherwise, NetMHCpan is the best predictor. When an allele has not been characterized, the performance depends on the distance to the training data. NetMHCpan has the highest performance when close neighbours are present in the training set, while the combination of NetMHCpan and PickPocket outperforms either of the two methods for alleles with more remote neighbours. The final method, NetMHCcons, is publicly available at www.cbs.dtu.dk/services/NetMHCcons , and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule.