Arginine (Di)methylated Human Leukocyte Antigen Class I Peptides Are Favorably Presented by HLA-B*07

A Chemical Approach to Assess the Impact of Post-translational Modification on MHC Peptide Binding and Effector Cell Engagement

The human major histocompatibility complex (MHC) plays a pivotal role in the presentation of peptidic fragments from proteins, which can originate from self-proteins or from nonhuman antigens, such as those produced by viruses or bacteria. To prevent cytotoxicity against healthy cells, thymocytes expressing T cell receptors (TCRs) that recognize self-peptides are removed from circulation (negative selection), thus leaving T cells that recognize nonself-peptides. Current understanding suggests that post-translationally modified (PTM) proteins and the resulting peptide fragments they generate following proteolysis are largely excluded from negative selection; this feature means that PTMs can generate nonself-peptides that potentially contribute to the development of autoreactive T cells and subsequent autoimmune diseases. Although it is well-established that PTMs are prevalent in peptides present on MHCs, the precise mechanisms by which PTMs influence the antigen presentation machinery remain poorly understood. In the present work, we introduce chemical modifications mimicking PTMs on synthetic peptides. This is the first systematic study isolating the impact of PTMs on MHC binding and also their impact on TCR recognition. Our findings reveal various ways PTMs alter antigen presentation, which could have implications for tumor neoantigen presentation.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

Gene and protein sequence features augment HLA class I ligand predictions

The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.

The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

The impact of immunopeptidomics: From basic research to clinical implementation

The immunopeptidome is the set of peptides presented by the major histocompatibility complex (MHC) molecules, in humans also known as the human leukocyte antigen (HLA), on the surface of cells that mediate T-cell immunosurveillance. The immunopeptidome is a sampling of the cellular proteome and hence it contains information about the health state of cells. The peptide repertoire is influenced by intra- and extra-cellular perturbations - such as in the case of drug exposure, infection, or oncogenic transformation. Immunopeptidomics is the bioanalytical method by which the presented peptides are extracted from biological samples and analyzed by high-performance liquid chromatography coupled to tandem mass spectrometry (MS), resulting in a deep qualitative and quantitative snapshot of the immunopeptidome. In this review, we discuss published immunopeptidomics studies from recent years, grouped into three main domains: i) basic, ii) pre-clinical and iii) clinical research and applications. We review selected fundamental immunopeptidomics studies on the antigen processing and presentation machinery, on HLA restriction and studies that advanced our understanding of various diseases, and how exploration of the antigenic landscape allowed immune targeting at the pre-clinical stage, paving the way to pioneering exploratory clinical trials where immunopeptidomics is directly implemented in the conception of innovative treatments for cancer patients.

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.

Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.

HLA-B and cysteinylated ligands distinguish the antigen presentation landscape of extracellular vesicles

Extracellular vesicles can modulate diverse processes ranging from proliferation and tissue repair, to chemo-resistance and cellular differentiation. With the advent of tissue and immunological targeting, extracellular vesicles are also increasingly viewed as promising vectors to deliver peptide-based cancer antigens to the human immune system. Despite the clinical relevance and therapeutic potential of such 'cell-free' approaches, the natural antigen presentation landscape exported in extracellular vesicles is still largely uncharted, due to the challenging nature of such preparations and analyses. In the context of therapeutic vesicle production, a critical evaluation of the similarity in vesicular antigen presentation is also urgently needed. In this work, we compared the HLA-I peptide ligandomes of extracellular vesicles against that of whole-cells of the same cell line. We found that extracellular vesicles not only over-represent HLA-B complexes and peptide ligands, but also cysteinylated peptides that may modulate immune responses. Collectively, these findings describe the pre-existing provision of vesicular HLA complexes that may be utilized to carry peptide vaccines, as well as the propensity for different peptide and post-translationally modified ligands to be presented, and will outline critical considerations in devising novel EV vaccination strategies.

Immunopeptidomics for next-generation bacterial vaccine development

Antimicrobial resistance is an increasing global threat and alternative treatments substituting failing antibiotics are urgently needed. Vaccines are recognized as highly effective tools to mitigate antimicrobial resistance; however, the selection of bacterial antigens as vaccine candidates remains challenging. In recent years, advances in mass spectrometry-based proteomics have led to the development of so-called immunopeptidomics approaches that allow the untargeted discovery of bacterial epitopes that are presented on the surface of infected cells. Especially for intracellular bacterial pathogens, immunopeptidomics holds great promise to uncover antigens that can be encoded in viral vector- or nucleic acid-based vaccines. This review provides an overview of immunopeptidomics studies on intracellular bacterial pathogens and considers future directions and challenges in advancing towards next-generation vaccines.

The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

The science that investigates the ensembles of all peptides associated to human leukocyte antigen (HLA) molecules is termed "immunopeptidomics" and is typically driven by mass spectrometry (MS) technologies. Recent advances in MS technologies, neoantigen discovery and cancer immunotherapy have catalyzed the launch of the Human Immunopeptidome Project (HIPP) with the goal of providing a complete map of the human immunopeptidome and making the technology so robust that it will be available in every clinic. Here, we provide a long-term perspective of the field and we use this framework to explore how we think the completion of the HIPP will truly impact the society in the future. In this context, we introduce the concept of immunopeptidome-wide association studies (IWAS). We highlight the importance of large cohort studies for the future and how applying quantitative immunopeptidomics at population scale may provide a new look at individual predisposition to common immune diseases as well as responsiveness to vaccines and immunotherapies. Through this vision, we aim to provide a fresh view of the field to stimulate new discussions within the community, and present what we see as the key challenges for the future for unlocking the full potential of immunopeptidomics in this era of precision medicine.

PRMTs and Arginine Methylation: Cancer's Best-Kept Secret?

Post-translational modification (PTM) of proteins is vital for increasing proteome diversity and maintaining cellular homeostasis. If the writing, reading, and removal of modifications are not controlled, cancer can develop. Arginine methylation is an understudied modification that is increasingly associated with cancer progression. Consequently protein arginine methyltransferases (PRMTs), the writers of arginine methylation, have rapidly gained interest as novel drug targets. However, for clinical success a deep mechanistic understanding of the biology of PRMTs is required. In this review we focus on advances made regarding the role of PRMTs in stem cell biology, epigenetics, splicing, immune surveillance and the DNA damage response, and highlight the rapid rise of specific inhibitors that are now in clinical trials for cancer therapy.

The SysteMHC Atlas project

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.

The Evolving Landscape of Autoantigen Discovery and Characterization in Type 1 Diabetes

Type 1 diabetes (T1D) is an autoimmune disease that is caused, in part, by T cell-mediated destruction of insulin-producing β-cells. High risk for disease, in those with genetic susceptibility, is predicted by the presence of two or more autoantibodies against insulin, the 65-kDa form of glutamic acid decarboxylase (GAD65), insulinoma-associated protein 2 (IA-2), and zinc transporter 8 (ZnT8). Despite this knowledge, we still do not know what leads to the breakdown of tolerance to these autoantigens, and we have an incomplete understanding of T1D etiology and pathophysiology. Several new autoantibodies have recently been discovered using innovative technologies, but neither their potential utility in monitoring disease development and treatment nor their role in the pathophysiology and etiology of T1D has been explored. Moreover, neoantigen generation (through posttranslational modification, the formation of hybrid peptides containing two distinct regions of an antigen or antigens, alternative open reading frame usage, and translation of RNA splicing variants) has been reported, and autoreactive T cells that target these neoantigens have been identified. Collectively, these new studies provide a conceptual framework to understand the breakdown of self-tolerance, if such modifications occur in a tissue- or disease-specific context. A recent workshop sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases brought together investigators who are using new methods and technologies to identify autoantigens and characterize immune responses toward these proteins. Researchers with diverse expertise shared ideas and identified resources to accelerate antigen discovery and the detection of autoimmune responses in T1D. The application of this knowledge will direct strategies for the identification of improved biomarkers for disease progression and treatment response monitoring and, ultimately, will form the foundation for novel antigen-specific therapeutics. This Perspective highlights the key issues that were addressed at the workshop and identifies areas for future investigation.

Pre-fractionation Extends but also Creates a Bias in the Detectable HLA Class Ι Ligandome

HLA class Ι molecules can communicate a range of cellular alterations (mutations, changes in protein copy number, aberrant post-translational modifications, or pathogen proteins) to CD8+ T lymphocytes in the form of HLA peptide ligands. At any given moment, tens of thousands of different self and foreign HLA class Ι peptides may be presented on the cell surface by HLA class Ι complexes. Due to the enormous biochemical diversity and low abundance of each of these peptides, HLA ligandome analysis presents unique challenges. Even with advances in enrichment strategies and MS instrumentation and fragmentation, sufficient ligandome depth for identification of viral pathogens and immuno therapeutically important tumor neo-antigens is still not routinely achievable. In this study, we evaluated two pre-fractionation techniques, high-pH reversed-phase and strong cation exchange, for the complementary analyses of HLA class Ι peptide ligands. We observe that pre-fractionation substantially extends the detectable HLA class Ι ligandome but also creates an identification bias. We thus advocate a rational choice between high-pH reversed-phase or strong cation exchange pre-fractionation for deeper HLA class Ι ligandome analysis, depending on the HLA locus, allele, or peptide ligand modification in question.

Synthetic T cell receptor-based lymphocytes for cancer therapy

Chimeric antigen receptor (CAR) T cells have been remarkably successful in patients with hematological malignancies expressing the CD19 surface antigen, but such level of success is far from being replicated in solid tumors. Engineered T cell receptor (TCR) T cells targeting cancer antigens were first developed over two decades ago and represent an alternative adoptive T cell approach that has produced provocative clinical data in solid cancers. However, several factors may hinder this technology from realizing its full potential, including the need for HLA matching, HLA downregulation by cancer cells, the suppressive tumor microenvironment, and tissue liabilities resulting from targeting antigens shared with normal tissues. Efforts therefore continue to engineer enhanced versions of CAR T and TCR T therapies that can overcome current barriers. Furthermore, emergent novel TCR-based, HLA-unrestricted platforms may also provide unique tools that integrate the complexity of the TCR signaling cascade that can be applied to treat solid tumors. This article reviews the current state of development of TCR T cell approaches and discusses next generation improvements to overcome their current limitations.

The co-activator-associated arginine methyltransferase 1 (CARM1) gene is overexpressed in type 2 diabetes

PURPOSE

We examined the expression of a panel of epigenetic enzymes catalyzing histone tails post-transcriptional modifications, together with effectors of metabolic and inflammatory alterations, in type 2 diabetes.

METHODS

Cross-sectional, case-control study of 21 people with type 2 diabetes and 21 matched controls. Total RNA was extracted from white cells and reverse transcribed. PCR primer assays for 84 key genes encoding enzymes known to modify genomic DNA and histones were performed. Western blot was performed on lysates using primary antibodies for abnormally expressed enzymes. Hormones and cytokines were measured by multiplex kits. A Bayesian network was built to investigate the relationships between epigenetic, cytokine, and endocrine variables.

RESULTS

Co-activator-associated aRginine Methyltransferase 1 (CARM1) expression showed a five-fold higher median value, matched by higher protein levels, among patients who also had increased GIP, IL-4, IL-7, IL-13, IL-17, FGF basic, G-CSF, IFN-γ, and TNFα and decreased IP-10. In a Bayesian network approach, CARM1 expression showed a conditional dependence on diabetes, but was independent of all other variables nor appeared to influence any.

CONCLUSIONS

Increased CARM1 expression in type 2 diabetes suggests that epigenetic mechanisms are altered in human diabetes. The impact of lifestyle and pharmacological treatment on regulation of this enzyme should be further investigated.

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome

BACKGROUND

To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I.

RESULTS

As measured by precision in the top 1% of predictions, our method outperforms NetMHC and NetMHCpan on test sets, and it outperforms both these methods and MixMHCpred on new data from an ovarian carcinoma cell line. We also find that random forest scores correlate monotonically, but not linearly, with known chemical binding affinities, and an information-based analysis of classifier features shows the importance of anchor positions for our classification. The random-forest approach also outperforms a deep neural network and a convolutional neural network trained on identical data. Finally, we use our large database to confirm that gene expression partially determines peptide presentation.

CONCLUSIONS

ForestMHC is a promising method to identify peptides bound by MHC-I. We have demonstrated the utility of random forest-based approaches in predicting peptide presentation by MHC-I, assembled the largest known database of MS binding data, and mined this database to show the effect of gene expression on peptide presentation. ForestMHC has potential applicability to basic immunology, rational vaccine design, and neoantigen binding prediction for cancer immunotherapy. This method is publicly available for applications and further validation.

Employing proteomics in the study of antigen presentation: an update

INTRODUCTION

Our immune system discriminates self from non-self by examining the peptide cargo of human leukocyte antigen (HLA) molecules displayed on the cell surface. Successful recognition of HLA-bound non-self peptides can induce T cell responses leading to, for example, the destruction of infected cells. Today, largely due to advances in technology, we have an unprecedented capability to identify the nature of these presented peptides and unravel the true complexity of antigen presentation. Areas covered: In addition to conventional linear peptides, HLA molecules also present post-translationally modified sequences comprising a wealth of chemical and structural modifications, including a novel class of noncontiguous spliced peptides. This review focuses on these emerging themes in antigen presentation and how mass spectrometry in particular has contributed to a new view of the antigenic landscape that is presented to the immune system. Expert Commentary: Advances in the sensitivity of mass spectrometers and use of hybrid fragmentation technologies will provide more information-rich spectra of HLA bound peptides leading to more definitive identification of T cell epitopes. Coupled with improvements in sample preparation and new informatics workflows, studies will access novel classes of peptide antigen and allow interrogation of rare and clinically relevant samples.

Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.

Minimal Information About an Immuno-Peptidomics Experiment (MIAIPE)

Minimal information about an immuno-peptidomics experiment (MIAIPE) is an initiative of the members of the Human Immuno-Peptidome Project (HIPP), an international program organized by the Human Proteome Organization (HUPO). The aim of the MIAIPE guidelines is to deliver technical guidelines representing the minimal information required to sufficiently support the evaluation and interpretation of immunopeptidomics experiments. The MIAIPE document has been designed to report essential information about sample preparation, mass spectrometric measurement, and associated mass spectrometry (MS)-related bioinformatics aspects that are unique to immunopeptidomics and may not be covered by the general proteomics MIAPE (minimal information about a proteomics experiment) guidelines.

Gaining Insight Into Posttranslationally Modified HIV Antigens and Their Underlying Characteristics

Mass spectrometry (MS)-based immunopeptidomics has developed as one of the leading methodologies for comprehensive characterization of in vivo presented human leukocyte antigen (HLA)-bound peptides. Unveiling the identity of HLA-bound peptides derived from diseased cells is crucial to gain knowledge on the constitution of efficient disease-specific T cell responses. The HLA-presented peptidome reflects the status of the cellular proteome, hence disease-related aberrations of posttranslational modifications (PTMs) might lead to presentation of peptides harboring PTMs. Therefore, characterization of HLA-bound PTM peptides could shed light on their relevance in immune and disease processes. In this issue, Ramarathinam et al. investigate the presentation of HIV envelope (HIVenv) peptides bound to the HLA-B*57:01 allele. Among these peptides, the authors specifically focused on a kynurenine-modified peptide. To this end, they characterize the possible origin of the kynurenine modification, its effect on HLA binding affinity, stability, conformation within the complex, and its immunogenicity compared to the native counterpart.

The Human Leukocyte Antigen (HLA)-B27 Peptidome in Vivo, in Spondyloarthritis-susceptible HLA-B27 Transgenic Rats and the Effect of Erap1 Deletion

HLA-B27 is a class I major histocompatibility (MHC-I) allele that confers susceptibility to the rheumatic disease ankylosing spondylitis (AS) by an unknown mechanism. ERAP1 is an aminopeptidase that trims peptides in the endoplasmic reticulum for binding to MHC-I molecules. ERAP1 shows genetic epistasis with HLA-B27 in conferring susceptibility to AS. Male HLA-B27 transgenic rats develop arthritis and serve as an animal model of AS, whereas female B27 transgenic rats remain healthy. We used large scale quantitative mass spectrometry to identify over 15,000 unique HLA-B27 peptide ligands, isolated after immunoaffinity purification of the B27 molecules from the spleens of HLA-B27 transgenic rats. Heterozygous deletion of Erap1, which reduced the Erap1 level to less than half, had no qualitative or quantitative effects on the B27 peptidome. Homozygous deletion of Erap1 affected approximately one-third of the B27 peptidome but left most of the B27 peptidome unchanged, suggesting the possibility that some of the HLA-B27 immunopeptidome is not processed in the presence of Erap1. Deletion of Erap1 was permissive for the AS-like phenotype, increased mean peptide length and increased the frequency of C-terminal hydrophobic residues and of N-terminal Ala, Ser, or Lys. The presence of Erap1 increased the frequency of C-terminal Lys and Arg, of Glu and Asp at intermediate residues, and of N-terminal Gly. Several peptides of potential interest in AS pathogenesis, previously identified in human cell lines, were isolated. However, rats susceptible to arthritis had B27 peptidomes similar to those of non-susceptible rats, and no peptides were found to be uniquely associated with arthritis. Whether specific B27-bound peptides are required for AS pathogenesis remains to be determined. Data are available via ProteomeXchange with identifier PXD005502.