Human T cells recognize HLA-DP-bound peptides in two orientations

Anti-HLA antibodies may be a subset of polyreactive immunoglobulins generated after viral superinfection

Chronic rejection remains an obstacle to long-term allograft survival. Donor-specific anti-HLA antibodies (DSA) play a significant role in causing chronic antibody-mediated allograft rejection. Exposure to mismatched HLA antigens via transfusion, pregnancy, or transplanted tissue has been described in the literature as an immunogenic stimulus of anti-HLA antibodies. Yet anti-HLA antibodies also develop in the absence of traditional sensitization events and molecular mimicry has been postulated as a stimulus for these naturally occurring alloantibodies. While heterologous reactivity has been documented between virus components and allogeneic T cells, there is insufficient evidence to support the development of anti-HLA antibodies from viral components. We hypothesized that anti-HLA antibodies may develop following viral coinfection or superinfection. The objectives of this investigation included: 1) developing an in-silico algorithm to identify viral peptide components that exhibit HLA-specific homology, and 2) identifying cellular changes that take place during ischemia/reperfusion injury which could facilitate the generation of novel anti-HLA antibodies from viral sources. We developed the neoepitope transplant rejection and autoimmune disease (NETRAD) algorithm to identify amino acid sequence homology between viral envelope proteins and HLA. The algorithm integrates post-translational protein modifications that are consistent with ischemia/reperfusion injury. Seventy-two HLA-specific epitopes were demarcated as examples using this approach. In conclusion, we present in-silico evidence which supports the identification of anti-HLA antibodies as a subset of polyreactive antibodies generated from stress-modified viral envelope proteins. Remarkably, each targeted HLA epitope associated with a distinct anti-HLA antibody could be consistently attributed to a major envelope glycoprotein component of Epstein Barr virus. Transplant Immunology manuscript # TRIM-D-24-00351. Dryad data repository:https://doi.org/10.5061/dryad.qjq2bvqpq.

Predicting reverse-bound peptide conformations in MHC Class II with PANDORA

Recent discoveries have transformed our understanding of peptide binding in Major Histocompatibility Complex (MHC) molecules, showing that peptides, for some MHC class II alleles, can bind in a reverse orientation (C-terminus to N-terminus) and can still effectively activate CD4+ T cells. These finding challenges established concepts of immune recognition and suggests new pathways for therapeutic intervention, such as vaccine design. We present an updated version of PANDORA, which, to the best of our knowledge, is the first tool capable of modeling reversed-bound peptides. Modeling these peptides presents a unique challenge due to the limited structural data available for these orientations in existing databases. PANDORA has overcome this challenge through integrative modeling using algorithmically reversed peptides as templates. We have validated the new PANDORA feature through two targeted experiments, achieving an average backbone binding-core L-RMSD value of 0.63 Å. Notably, it maintained low RMSD values even when using templates from different alleles and peptide sequences. Our results suggest that PANDORA will be an invaluable resource for the immunology community, aiding in the development of targeted immunotherapies and vaccine design.

Effect of peptide-binding motif on survival of HLA-haploidentical transplantation with post-transplant cyclophosphamide

Peptide-binding motif (PBM) model, a hierarchical clustering of HLA class I based on their binding specificity, was developed to predict immunopeptidome divergence. The effect of PBM mismatches on outcomes is unknown in HLA-haploidentical haematopoietic cell transplantation with post-transplant cyclophosphamide (PTCy-haplo). We therefore conducted a retrospective study using national registry data in PTCy-haplo. Overall, 1352 patients were included in the study. PBM-A bidirectional mismatch was associated with an increased risk of overall mortality in multivariable analysis (hazard ratio, 1.26; 95% confidence interval, 1.06 to 1.50; p = 0.010). None of relapse, non-relapse mortality (NRM) and graft-versus-host disease showed significant differences according to PBM-A bidirectional mismatch status in the entire cohort. The impact of PBM-A bidirectional mismatch on overall survival (OS) was preserved within the HLA-A genotype bidirectional mismatch population, and their lower OS stemmed from higher relapse rate in this population. The worse OS due to high NRM with PBM-A bidirectional mismatch was prominent in lymphoid malignancies receiving reduced-intensity conditioning. The PBM model may predict outcomes more accurately than HLA genotype mismatches. In conclusion, this study demonstrated that the presence of PBM-A bidirectional mismatch elevated the risk of mortality of PTCy-haplo. Avoiding PBM-A bidirectional mismatch might achieve better outcomes in PTCy-haplo.

HLA Class II (DR, DQ, DP) Genes Were Separately Associated With the Progression From Seroconversion to Onset of Type 1 Diabetes Among Participants in Two Diabetes Prevention Trials (DPT-1 and TN07)

OBJECTIVE

To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Next-generation targeted sequencing was used to genotype eight HLAII genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression.

RESULTS

First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio [HR] = 1.25, P = 3.50*10-3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10-3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ, DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10-3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02, sharing the DRB1*03:01, had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression.

CONCLUSIONS

These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies.

From an understanding of etiopathogenesis to novel therapies-what is new in the treatment of celiac disease?

Celiac disease, a chronic autoimmune disorder caused by genetic factors and exposure to gluten, is increasingly being recognized and diagnosed in both children and adults. Scientists have been searching for a cure for this disease for many years, but despite the impressive development of knowledge in this field, a gluten-free diet remains the only recommended therapy for all patients. At the same time, the increasing diagnosis of celiac disease in adults, which was considered a childhood disease in the 20th century, has opened a discussion on the etiopathology of the disease, which is proven to be very complex and involves genetic, immunological, nutritional, environmental and gut microbiota-related factors. In this review, we extensively discuss these factors and summarize the knowledge of the proposed state-of-the-art treatments for celiac disease to address the question of whether a better understanding of the etiopathogenesis of celiac disease has opened new directions for therapy.

STING agonist diABZI enhances the cytotoxicity of T cell towards cancer cells

Antigen-specific T cell receptor-engineered T cell (TCR-T) based immunotherapy has proven to be an effective method to combat cancer. In recent years, cross-talk between the innate and adaptive immune systems may be requisite to optimize sustained antigen-specific immunity, and the stimulator of interferon genes (STING) is a promising therapeutic target for cancer immunotherapy. The level of expression or presentation of antigen in tumor cells affects the recognition and killing of tumor cells by TCR-T. This study aimed at investigating the potential of innate immune stimulation of T cells and engineered T cells to enhance immunotherapy for low-expression antigen cancer cells. We systematically investigated the function and mechanism of cross-talk between STING agonist diABZI and adaptive immune systems. We established NY-ESO-1 full knockout Mel526 cells for this research and found that diABZI activated STING media and TCR signaling pathways. In addition, the results of flow cytometry showed that antigens presentation from cancer cells induced by STING agonist diABZI also improved the affinity of TCR-T cells function against tumor cells in vitro and in vivo. Our findings revealed that diABZI enhanced the immunotherapy efficacy of TCR-T by activating STING media and TCR signaling pathways, improving interferon-γ expression, and increasing antigens presentation of tumor cells. This indicates that STING agonist could be used as a strategy to promote TCR-T cancer immunotherapy.

MORITS: An improved method to predict peptides from heterologous proteins that are recognized by the same T-cell receptor

Antigen-specific priming of T cells results in the activation of T cells that exert effector functions by interaction of their T-cell receptor (TCR) with the corresponding self-MHC molecule presenting a peptide on the surface of a target cell. Such antigen-specific T cells potentially can also interact with peptide-MHC complexes that contain peptides from unrelated antigens, a phenomenon that often is referred to as heterologous immunity. For example, some individuals that were pre-immunized against an allergen, could subsequently mount better anti-viral T-cell responses than non-allergic individuals. So far only few peptide pairs that experimentally have been shown to provoke heterologous immunity were  identified, and available prediction tools that can identify potential candidates are imprecise. We developed the MORITS algorithm to rapidly screen large lists of peptides for sequence similarities, while giving enhanced consideration to peptide residues presented by MHC that are particularly relevant for TCR interactions. In combination with established peptide-MHC binding prediction tools, the MORITS algorithm revealed peptide similarities between the SARS-CoV-2 proteome and certain allergens. The method outperformed previously published workflows and may help to identify novel pairs of peptides that mediate heterologous immune responses.

Accurate modeling of peptide-MHC structures with AlphaFold

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T cell surveillance. Reliable in silico prediction of which peptides would be presented and which T cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.

How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred

MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .

Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

Structural definition of HLA class II-presented SARS-CoV-2 epitopes reveals a mechanism to escape pre-existing CD4+ T cell immunity

CD4+ T cells recognize a broad range of peptide epitopes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which contribute to immune memory and limit COVID-19 disease. We demonstrate that the immunogenicity of SARS-CoV-2 peptides, in the context of the model allotype HLA-DR1, does not correlate with their binding affinity to the HLA heterodimer. Analyzing six epitopes, some with very low binding affinity, we solve X-ray crystallographic structures of each bound to HLA-DR1. Further structural definitions reveal the precise molecular impact of viral variant mutations on epitope presentation. Omicron escaped ancestral SARS-CoV-2 immunity to two epitopes through two distinct mechanisms: (1) mutations to TCR-facing epitope positions and (2) a mechanism whereby a single amino acid substitution caused a register shift within the HLA binding groove, completely altering the peptide-HLA structure. This HLA-II-specific paradigm of immune escape highlights how CD4+ T cell memory is finely poised at the level of peptide-HLA-II presentation.

HLA-DP diversity is associated with improved response to SARS-Cov-2 vaccine in hematopoietic stem cell transplant recipients

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipients show lower humoral vaccine responsiveness than immunocompetent individuals. HLA diversity, measured by the HLA evolutionary divergence (HED) metrics, reflects the diversity of the antigenic repertoire presented to T cells, and has been shown to predict response to cancer immunotherapy. We retrospectively investigated the association of HED with humoral response to SARS-CoV-2 vaccine in allo-HSCT recipients. HED was calculated as pairwise genetic distance between alleles at HLA-A, -B, -C, -DRB1, -DQB1, and -DPB1 loci in recipients and their donors. Low anti-spike IgG levels (<30 BAU/mL) were associated with short time from allo-SCT and low donor DPB1-HED, mostly related to donor DPB1 homozygosity. The diversity of donor HLA-DP molecules, assessed by heterozygosity or sequence divergence, may thus impact the efficacy of donor-derived CD4 T cells to sustain vaccine-mediated antibody response in allo-HSCT recipients.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.