A comprehensive analysis of constitutive naturally processed and presented HLA-C*04:01 (Cw4)-specific peptides

MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes

The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

T Cell Epitope Discovery in the Context of Distinct and Unique Indigenous HLA Profiles

CD8⁺ T cells are a pivotal part of the immune response to viruses, playing a key role in disease outcome and providing long-lasting immunity to conserved pathogen epitopes. Understanding CD8⁺ T cell immunity in humans is complex due to CD8⁺ T cell restriction by highly polymorphic Human Leukocyte Antigen (HLA) proteins, requiring T cell epitopes to be defined for different HLA allotypes across different ethnicities. Here we evaluate strategies that have been developed to facilitate epitope identification and study immunogenic T cell responses. We describe an immunopeptidomics approach to sequence HLA-bound peptides presented on virus-infected cells by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Using antigen presenting cell lines that stably express the HLA alleles characteristic of Indigenous Australians, this approach has been successfully used to comprehensively identify influenza-specific CD8⁺ T cell epitopes restricted by HLA allotypes predominant in Indigenous Australians, including HLA-A*24:02 and HLA-A*11:01. This is an essential step in ensuring high vaccine coverage and efficacy in Indigenous populations globally, known to be at high risk from influenza disease and other respiratory infections.

Identification of tumor antigens with immunopeptidomics

The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor-transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens-antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.

Immunopeptidomics toolkit library (IPTK): a python-based modular toolbox for analyzing immunopeptidomics data

Background

The human leukocyte antigen (HLA) proteins play a fundamental role in the adaptive immune system as they present peptides to T cells. Mass-spectrometry-based immunopeptidomics is a promising and powerful tool for characterizing the immunopeptidomic landscape of HLA proteins, that is the peptides presented on HLA proteins. Despite the growing interest in the technology, and the recent rise of immunopeptidomics-specific identification pipelines, there is still a gap in data-analysis and software tools that are specialized in analyzing and visualizing immunopeptidomics data.

Results

We present the IPTK library which is an open-source Python-based library for analyzing, visualizing, comparing, and integrating different omics layers with the identified peptides for an in-depth characterization of the immunopeptidome. Using different datasets, we illustrate the ability of the library to enrich the result of the identified peptidomes. Also, we demonstrate the utility of the library in developing other software and tools by developing an easy-to-use dashboard that can be used for the interactive analysis of the results.

Conclusion

IPTK provides a modular and extendable framework for analyzing and integrating immunopeptidomes with different omics layers. The library is deployed into PyPI at https://pypi.org/project/IPTKL/ and into Bioconda at https://anaconda.org/bioconda/iptkl, while the source code of the library and the dashboard, along with the online tutorials are available at https://github.com/ikmb/iptoolkit.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04315-0.

Identification of Flucloxacillin-Haptenated HLA-B*57:01 Ligands: Evidence of Antigen Processing and Presentation

Flucloxacillin is a β-lactam antibiotic associated with a high incidence of drug-induced liver reactions. Although expression of human leukocyte antigen (HLA)-B*57:01 increases susceptibility, little is known of the pathological mechanisms involved in the induction of the clinical phenotype. Irreversible protein modification is suspected to drive the reaction through the modification of peptides that are presented by the risk allele. In this study, the binding of flucloxacillin to immune cells was characterized and the nature of the peptides presented by HLA-B*57:01 was analyzed using mass spectrometric-based immunopeptidomics methods. Flucloxacillin modification of multiple proteins was observed, providing a potential source of neoantigens for HLA presentation. Of the peptides eluted from flucloxacillin-treated C1R-B*57:01 cells, 6 putative peptides were annotated as flucloxacillin-modified HLA-B*57:01 peptide ligands (data are available via ProteomeXchange with identifier PXD020137). To conclude, we have characterized naturally processed drug-haptenated HLA ligands presented on the surface of antigen presenting cells that may drive drug-specific CD8+ T-cell responses.

MHCVision: estimation of global and local false discovery rate for MHC class I peptide binding prediction

Motivation

MHC-peptide binding prediction has been widely used for understanding the immune response of individuals or populations, each carrying different MHC molecules as well as for the development of immunotherapeutics. The results from MHC-peptide binding prediction tools are mostly reported as a predicted binding affinity (IC₅₀) and the percentile rank score, and global thresholds e.g. IC₅₀ value < 500 nM or percentile rank < 2% are generally recommended for distinguishing binding peptides from non-binding peptides. However, it is difficult to evaluate statistically the probability of an individual peptide binding prediction to be true or false solely considering predicted scores. Therefore, statistics describing the overall global false discovery rate (FDR) and local FDR, also called posterior error probability (PEP) are required to give statistical context to the natively produced scores.

Result

We have developed an algorithm and code implementation, called MHCVision, for estimation of FDR and PEP values for the predicted results of MHC-peptide binding prediction from the NetMHCpan tool. MHCVision performs parameter estimation using a modified expectation maximization framework for a two-component beta mixture model, representing the distribution of true and false scores of the predicted dataset. We can then estimate the PEP of an individual peptide’s predicted score, and conversely the probability that it is true. We demonstrate that the use of global FDR and PEP estimation can provide a better trade-off between sensitivity and precision over using currently recommended thresholds from tools.

Availability and implementation

https://github.com/PGB-LIV/MHCVision.

Supplementary information

Supplementary data are available at Bioinformatics online.

Kinetics of Abacavir-Induced Remodelling of the Major Histocompatibility Complex Class I Peptide Repertoire

Abacavir hypersensitivity syndrome can occur in individuals expressing the HLA-B*57:01 major histocompatibility complex class I allotype when utilising the drug abacavir as a part of their anti-retroviral regimen. The drug is known to bind within the HLA-B*57:01 antigen binding cleft, leading to the selection of novel self-peptide ligands, thus provoking life-threatening immune responses. However, the sub-cellular location of abacavir binding and the mechanics of altered peptide selection are not well understood. Here, we probed the impact of abacavir on the assembly of HLA-B*57:01 peptide complexes. We show that whilst abacavir had minimal impact on the maturation or average stability of HLA-B*57:01 molecules, abacavir was able to differentially enhance the formation, selectively decrease the dissociation, and alter tapasin loading dependency of certain HLA-B*57:01-peptide complexes. Our data reveals a spectrum of abacavir mediated effects on the immunopeptidome which reconciles the heterogeneous functional T cell data reported in the literature.

DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information

BACKGROUND

Epitope prediction is a useful approach in cancer immunology and immunotherapy. Many computational methods, including machine learning and network analysis, have been developed quickly for such purposes. However, regarding clinical applications, the existing tools are insufficient because few of the predicted binding molecules are immunogenic. Hence, to develop more potent and effective vaccines, it is important to understand binding and immunogenic potential. Here, we observed that the interactive association constituted by human leukocyte antigen (HLA)-peptide pairs can be regarded as a network in which each HLA and peptide is taken as a node. We speculated whether this network could detect the essential interactive propensities embedded in HLA-peptide pairs. Thus, we developed a network-based deep learning method called DeepNetBim by harnessing binding and immunogenic information to predict HLA-peptide interactions.

RESULTS

Quantitative class I HLA-peptide binding data and qualitative immunogenic data (including data generated from T cell activation assays, major histocompatibility complex (MHC) binding assays and MHC ligand elution assays) were retrieved from the Immune Epitope Database database. The weighted HLA-peptide binding network and immunogenic network were integrated into a network-based deep learning algorithm constituted by a convolutional neural network and an attention mechanism. The results showed that the integration of network centrality metrics increased the power of both binding and immunogenicity predictions, while the new model significantly outperformed those that did not include network features and those with shuffled networks. Applied on benchmark and independent datasets, DeepNetBim achieved an AUC score of 93.74% in HLA-peptide binding prediction, outperforming 11 state-of-the-art relevant models. Furthermore, the performance enhancement of the combined model, which filtered out negative immunogenic predictions, was confirmed on neoantigen identification by an increase in both positive predictive value (PPV) and the proportion of neoantigen recognition.

CONCLUSIONS

We developed a network-based deep learning method called DeepNetBim as a pan-specific epitope prediction tool. It extracted the attributes of the network as new features from HLA-peptide binding and immunogenic models. We observed that not only did DeepNetBim binding model outperform other updated methods but the combination of our two models showed better performance. This indicates further applications in clinical practice.

In-Vitro Approaches to Predict and Study T-Cell Mediated Hypersensitivity to Drugs

Mitigating the risk of drug hypersensitivity reactions is an important facet of a given pharmaceutical, with poor performance in this area of safety often leading to warnings, restrictions and withdrawals. In the last 50 years, efforts to diagnose, manage, and circumvent these obscure, iatrogenic diseases have resulted in the development of assays at all stages of a drugs lifespan. Indeed, this begins with intelligent lead compound selection/design to minimize the existence of deleterious chemical reactivity through exclusion of ominous structural moieties. Preclinical studies then investigate how compounds interact with biological systems, with emphasis placed on modeling immunological/toxicological liabilities. During clinical use, competent and accurate diagnoses are sought to effectively manage patients with such ailments, and pharmacovigilance datasets can be used for stratification of patient populations in order to optimise safety profiles. Herein, an overview of some of the in-vitro approaches to predict intrinsic immunogenicity of drugs and diagnose culprit drugs in allergic patients after exposure is detailed, with current perspectives and opportunities provided.

The immunopeptidomes of two transmissible cancers and their host have a common, dominant peptide motif

Transmissible cancers are malignant cells that can spread between individuals of a population, akin to both a parasite and a mobile graft. The survival of the Tasmanian devil, the largest remaining marsupial carnivore, is threatened by the remarkable emergence of two independent lineages of transmissible cancer, devil facial tumour (DFT) 1 and devil facial tumour 2 (DFT2). To aid the development of a vaccine and to interrogate how histocompatibility barriers can be overcome, we analysed the peptides bound to major histocompatibility complex class I (MHC‐I) molecules from Tasmanian devil cells and representative cell lines of each transmissible cancer. Here, we show that DFT1 + IFN‐γ and DFT2 cell lines express a restricted repertoire of MHC‐I allotypes compared with fibroblast cells, potentially reducing the breadth of peptide presentation. Comparison of the peptidomes from DFT1 + IFNγ, DFT2 and host fibroblast cells demonstrates a dominant motif, despite differences in MHC‐I allotypes between the cell lines, with preference for a hydrophobic leucine residue at position 3 and position Ω of peptides. DFT1 and DFT2 both present peptides derived from neural proteins, which reflects a shared cellular origin that could be exploited for vaccine design. These results suggest that polymorphisms in MHC‐I molecules between tumours and host can be ‘hidden’ by a common peptide motif, providing the potential for permissive passage of infectious cells and demonstrating complexity in mammalian histocompatibility barriers.

Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design

The features of peptide antigens that contribute to their immunogenicity are not well understood. Although the stability of peptide-MHC (pMHC) is known to be important, current assays assess this interaction only for peptides in isolation and not in the context of natural antigen processing and presentation. Here, we present a method that provides a comprehensive and unbiased measure of pMHC stability for thousands of individual ligands detected simultaneously by mass spectrometry (MS). The method allows rapid assessment of intra-allelic and inter-allelic differences in pMHC stability and reveals profiles of stability that are broader than previously appreciated. The additional dimensionality of the data facilitated the training of a model which improves the prediction of peptide immunogenicity, specifically of cancer neoepitopes. This assay can be applied to any cells bearing MHC or MHC-like molecules, offering insight into not only the endogenous immunopeptidome, but also that of neoepitopes and pathogen-derived sequences.

Immunopeptidomic Analysis Reveals That Deamidated HLA-bound Peptides Arise Predominantly from Deglycosylated Precursors

The presentation of post-translationally modified (PTM) peptides by cell surface HLA molecules has the potential to increase the diversity of targets for surveilling T cells. Although immunopeptidomics studies routinely identify thousands of HLA-bound peptides from cell lines and tissue samples, in-depth analyses of the proportion and nature of peptides bearing one or more PTMs remains challenging. Here we have analyzed HLA-bound peptides from a variety of allotypes and assessed the distribution of mass spectrometry-detected PTMs, finding deamidation of asparagine or glutamine to be highly prevalent. Given that asparagine deamidation may arise either spontaneously or through enzymatic reaction, we assessed allele-specific and global motifs flanking the modified residues. Notably, we found that the N-linked glycosylation motif NX(S/T) was highly abundant across asparagine-deamidated HLA-bound peptides. This finding, demonstrated previously for a handful of deamidated T cell epitopes, implicates a more global role for the retrograde transport of nascently N-glycosylated polypeptides from the ER and their subsequent degradation within the cytosol to form HLA-ligand precursors. Chemical inhibition of Peptide:N-Glycanase (PNGase), the endoglycosidase responsible for the removal of glycans from misfolded and retrotranslocated glycoproteins, greatly reduced presentation of this subset of deamidated HLA-bound peptides. Importantly, there was no impact of PNGase inhibition on peptides not containing a consensus NX(S/T) motif. This indicates that a large proportion of HLA-I bound asparagine deamidated peptides are generated from formerly glycosylated proteins that have undergone deglycosylation via the ER-associated protein degradation (ERAD) pathway. The information herein will help train deamidation prediction models for HLA-peptide repertoires and aid in the design of novel T cell therapeutic targets derived from glycoprotein antigens.

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.

Allelic association with ankylosing spondylitis fails to correlate with human leukocyte antigen B27 homodimer formation

Expression of human leukocyte antigen (HLA)-B27 is strongly associated with predisposition toward ankylosing spondylitis (AS) and other spondyloarthropathies. However, the exact involvement of HLA-B27 in disease initiation and progression remains unclear. The homodimer theory, which proposes that HLA-B27 heavy chains aberrantly form homodimers, is a central hypothesis that attempts to explain the role of HLA-B27 in disease pathogenesis. Here, we examined the ability of the eight most prevalent HLA-B27 allotypes (HLA-B*27:02 to HLA-B*27:09) to form homodimers. We observed that HLA-B*27:03, a disease-associated HLA-B27 subtype, showed a significantly reduced ability to form homodimers compared with all other allotypes, including the non-disease-associated/protective allotypes HLA-B*27:06 and HLA-B*27:09. We used X-ray crystallography and site-directed mutagenesis to unravel the molecular and structural mechanisms in HLA-B*27:03 that are responsible for its compromised ability to form homodimers. We show that polymorphism at position 59, which differentiates HLA-B*27:03 from all other allotypes, is responsible for its compromised ability to form homodimers. Indeed, histidine 59 in HLA-B*27:03 leads to a series of local conformational changes that act in concert to reduce the accessibility of the nearby cysteine 67, an essential amino acid residue for the formation of HLA-B27 homodimers. Considered together, the ability of both protective and disease-associated HLA-B27 allotypes to form homodimers and the failure of HLA-B*27:03 to form homodimers challenge the role of HLA-B27 homodimers in AS pathoetiology. Rather, this work implicates other features, such as peptide binding and antigen presentation, as pivotal mechanisms for disease pathogenesis.

Substantial Influence of ERAP2 on the HLA-B*40:02 Peptidome: Implications for HLA-B*27-Negative Ankylosing Spondylitis

HLA-B*40:02 is one of a few major histocompatibility complex class I (MHC-I) molecules associated with ankylosing spondylitis (AS) independently of HLA-B*27. The endoplasmic reticulum aminopeptidase 2 (ERAP2), an enzyme that process MHC-I ligands and preferentially trims N-terminal basic residues, is also a risk factor for this disease. Like HLA-B*27 and other AS-associated MHC-I molecules, HLA-B*40:02 binds a relatively high percentage of peptides with ERAP2-susceptible residues. In this study, the effects of ERAP2 depletion on the HLA-B*40:02 peptidome were analyzed. ERAP2 protein expression was knocked out by CRISPR in the transfectant cell line C1R-B*40:02, and the differences between the peptidomes from the wild-type and ERAP2-KO cells were determined by label-free quantitative comparisons. The qualitative changes dependent on ERAP2 affected about 5% of the peptidome, but quantitative changes in peptide amounts were much more substantial, reflecting a significant influence of this enzyme on the generation/destruction balance of HLA-B*40:02 ligands. As in HLA-B*27, a major effect was on the frequencies of N-terminal residues. In this position, basic and small residues were increased, and aliphatic/aromatic ones decreased in the ERAP2 knockout. Other peptide positions were also affected. Because most of the non-B*27 MHC-I molecules associated with AS risk bind a relatively high percentage of peptides with N-terminal basic residues, we hypothesize that the non-epistatic association of ERAP2 with AS might be related to the processing of peptides with these residues, thus affecting the peptidomes of AS-associated MHC-I molecules.

A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands

The diversity of peptides displayed by class I human leukocyte antigen (HLA) plays an essential role in T cell immunity. The peptide repertoire is extended by various posttranslational modifications, including proteasomal splicing of peptide fragments from distinct regions of an antigen to form nongenomically templated cis-spliced sequences. Previously, it has been suggested that a fraction of the immunopeptidome constitutes such cis-spliced peptides; however, because of computational limitations, it has not been possible to assess whether trans-spliced peptides (i.e., the fusion of peptide segments from distinct antigens) are also bound and presented by HLA molecules, and if so, in what proportion. Here, we have developed and applied a bioinformatic workflow and demonstrated that trans-spliced peptides are presented by HLA-I, and their abundance challenges current models of proteasomal splicing that predict cis-splicing as the most probable outcome. These trans-spliced peptides display canonical HLA-binding sequence features and are as frequently identified as cis-spliced peptides found bound to a number of different HLA-A and HLA-B allotypes. Structural analysis reveals that the junction between spliced peptides is highly solvent exposed and likely to participate in T cell receptor interactions. These results highlight the unanticipated diversity of the immunopeptidome and have important implications for autoimmunity, vaccine design, and immunotherapy.

Mass spectrometry-based identification of MHC-bound peptides for immunopeptidomics

Peptide antigens bound to molecules encoded by the major histocompatibility complex (MHC) and presented on the cell surface form the targets of T lymphocytes. This critical arm of the adaptive immune system facilitates the eradication of pathogen-infected and cancerous cells, as well as the production of antibodies. Methods to identify these peptide antigens are critical to the development of new vaccines, for which the goal is the generation of effective adaptive immune responses and long-lasting immune memory. Here, we describe a robust protocol for the identification of MHC-bound peptides from cell lines and tissues, using nano-ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (nUPLC-MS/MS) and recent improvements in methods for isolation and characterization of these peptides. The protocol starts with the immunoaffinity capture of naturally processed MHC-peptide complexes. The peptides dissociate from the class I human leukocyte antigens (HLAs) upon acid denaturation. This peptide cargo is then extracted and separated into fractions by HPLC, and the peptides in these fractions are identified using nUPLC-MS/MS. With this protocol, several thousand peptides can be identified from a wide variety of cell types, including cancerous and infected cells and those from tissues, with a turnaround time of 2-3 d.

HLA-B57 micropolymorphism defines the sequence and conformational breadth of the immunopeptidome

Immunophenotypic differences between closely related human leukocyte antigen (HLA) alleles have been associated with divergent clinical outcomes in infection, autoimmunity, transplantation and drug hypersensitivity. Here we explore the impact of micropolymorphism on peptide antigen presentation by three closely related HLA molecules, HLA-B*57:01, HLA-B*57:03 and HLA-B*58:01, that are differentially associated with the HIV elite controller phenotype and adverse drug reactions. For each allotype, we mine HLA ligand data sets derived from the same parental cell proteome to define qualitative differences in peptide presentation using classical peptide binding motifs and an unbiased statistical approach. The peptide repertoires show marked qualitative overlap, with 982 peptides presented by all allomorphs. However, differences in peptide abundance, HLA-peptide stability, and HLA-bound conformation demonstrate that HLA micropolymorphism impacts more than simply the range of peptide ligands. These differences provide grounds for distinct immune reactivity and insights into the capacity of micropolymorphism to diversify immune outcomes.

Human leukocyte antigens (HLA) are multi-allelic and polymorphic genes that present antigens to immune cells for inducing protective immunity. Here, using systems biology and structural approaches, the authors show that micropolymorphism of three HLA has effects beyond the modulation of antigen diversity.

The use of proteomics to understand antiviral immunity

Viruses are intracellular pathogens that cause a vast array of diseases, which are often severe and typified by high morbidity and mortality rates. Viral infections continue to be a global health burden and effective vaccines and therapeutics are constantly sought to prevent and treat these infections. The development of such treatments generally relies on understanding the mechanisms that underpin efficient host antiviral immune responses. This review summarises recent developments in our understanding of antiviral adaptive immunity and in particular, highlights the use of mass spectrometry to elucidate viral antigens and their processing and presentation to T cells and other immune effectors. These processed peptides serve as potential vaccine candidates or may facilitate clinical monitoring, diagnosis and immunotherapy of infectious diseases.

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.

Identification of Native and Posttranslationally Modified HLA-B*57:01-Restricted HIV Envelope Derived Epitopes Using Immunoproteomics

The recognition of pathogen-derived peptides by T lymphocytes is the cornerstone of adaptive immunity, whereby intracellular antigens are degraded in the cytosol and short peptides assemble with class I human leukocyte antigen (HLA) molecules in the ER. These peptide-HLA complexes egress to the cell surface and are scrutinized by cytotoxic CD8+ T-cells leading to the eradication of the infected cell. Here, naturally presented HLA-B*57:01 bound peptides derived from the envelope protein of the human immunodeficiency virus (HIVenv) are identified. HIVenv peptides are present at a very small percentage of the overall HLA-B*57:01 peptidome (<0.1%) and both native and posttranslationally modified forms of two distinct HIV peptides are identified. Notably, a peptide bearing a natively encoded C-terminal tryptophan residue is also present in a modified form containing a kynurenine residue. Kynurenine is a major product of tryptophan catabolism and is abundant during inflammation and infection. Binding of these peptides at a molecular level and their immunogenicity in preliminary functional studies are examined. Modest immune responses are observed to the modified HIVenv peptide, highlighting a potential role for kynurenine-modified peptides in the immune response to HIV and other viral infections.

Unveiling the Peptide Motifs of HLA-C and HLA-G from Naturally Presented Peptides and Generation of Binding Prediction Matrices

The classical HLA-C and the nonclassical HLA-E and HLA-G molecules play important roles both in the innate and adaptive immune system. Starting already during embryogenesis and continuing throughout our lives, these three Ags exert major functions in immune tolerance, defense against infections, and anticancer immune responses. Despite these important roles, identification and characterization of the peptides presented by these molecules has been lacking behind the more abundant HLA-A and HLA-B gene products. In this study, we elucidated the peptide specificities of these HLA molecules using a comprehensive analysis of naturally presented peptides. To that end, the 15 most frequently expressed HLA-C alleles as well as HLA-E*01:01 and HLA-G*01:01 were transfected into lymphoblastoid C1R cells expressing low endogenous HLA. Identification of naturally presented peptides was performed by immunoprecipitation of HLA and subsequent analysis of HLA-bound peptides by liquid chromatographic tandem mass spectrometry. Peptide motifs of HLA-C unveil anchors in position 2 or 3 with high variances between allotypes, and a less variable anchor at the C-terminal end. The previously reported small ligand repertoire of HLA-E was confirmed within our analysis, and we could show that HLA-G combines a large ligand repertoire with distinct features anchoring peptides at positions 3 and 9, supported by an auxiliary anchor in position 1 and preferred residues in positions 2 and 7. The wealth of HLA ligands resulted in prediction matrices for octa-, nona-, and decamers. Matrices were validated in terms of their binding prediction and compared with the latest NetMHC prediction algorithm NetMHCpan-3.0, which demonstrated their predictive power.

The molecular basis for peptide repertoire selection in the human leucocyte antigen (HLA) C*06:02 molecule

Human leukocyte antigen (HLA)-C*06:02 is identified as the allele associated with the highest risk for the development of the autoimmune skin disease psoriasis. However, the diversity and mode of peptide presentation by the HLA-C*06:02 molecule remains unclear. Here, we describe the endogenous peptide repertoire of ∼3,000 sequences for HLA-C*06:02 that defines the peptide-binding motif for this HLA allomorph. We found that HLA-C*06:02 predominantly presents nonamer peptides with dominant arginine anchors at the P2 and P7 positions and a preference for small hydrophobic residues at the C terminus (PΩ). To determine the structural basis of this selectivity, we determined crystal structures of HLA-C*06:02 in complex with two self-peptides (ARTELYRSL and ARFNDLRFV) and an analogue of a melanocyte autoantigen (ADAMTSL5, VRSRR-abu-LRL) implicated in psoriasis. These structures revealed that HLA-C*06:02 possesses a deep peptide-binding groove comprising two electronegative B- and E-pockets that coincide with the preference for P2 and P7 arginine anchors. The ADAMTSL5 autoantigen possessed a P7-Leu instead of the P7-Arg residue, but nevertheless was accommodated within the HLA-C*06:02 antigen-binding cleft. Collectively, our results provide the structural basis for understanding peptide repertoire selection in HLA-C*06:02.

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.

Predicting the differences between cancer and normal cells that are visible to the immune system is of central importance for cancer immunotherapy. Here we introduce a novel computational framework to harness the wealth of data from in-depth HLA peptidomics studies, including ten novel high quality (<1% FDR) datasets generated for this work, to improve predictions of peptides displayed on HLA-I molecules. These high-throughput and unbiased data enable us to refine models of HLA-I binding specificity for many alleles (including some that had no ligand until this study) and improve predictions of neo-antigens from exome sequencing data in melanoma and lung cancer samples. Moreover, the refined description of HLA-I binding specificity reveals cases of allosteric modulation of HLA-I binding specificity at the second amino acid position (P2) of their ligands by residues that are part of the HLA-I binding site but outside of the B pocket.

Structural and regulatory diversity shape HLA-C protein expression levels

Expression of HLA-C varies widely across individuals in an allele-specific manner. This variation in expression can influence efficacy of the immune response, as shown for infectious and autoimmune diseases. MicroRNA binding partially influences differential HLA-C expression, but the additional contributing factors have remained undetermined. Here we use functional and structural analyses to demonstrate that HLA-C expression is modulated not just at the RNA level, but also at the protein level. Specifically, we show that variation in exons 2 and 3, which encode the α1/α2 domains, drives differential expression of HLA-C allomorphs at the cell surface by influencing the structure of the peptide-binding cleft and the diversity of peptides bound by the HLA-C molecules. Together with a phylogenetic analysis, these results highlight the diversity and long-term balancing selection of regulatory factors that modulate HLA-C expression.

Dominant protection from HLA-linked autoimmunity by antigen-specific regulatory T cells

Susceptibility and protection against human autoimmune diseases, including type I diabetes, multiple sclerosis, and Goodpasture disease, is associated with particular human leukocyte antigen (HLA) alleles. However, the mechanisms underpinning such HLA-mediated effects on self-tolerance remain unclear. Here we investigate the molecular mechanism of Goodpasture disease, an HLA-linked autoimmune renal disorder characterized by an immunodominant CD4⁺ T-cell self-epitope derived from the α3 chain of type IV collagen (α3_135-145). While HLA-DR15 confers a markedly increased disease risk, the protective HLA-DR1 allele is dominantly protective in trans with HLA-DR15 (ref. 2). We show that autoreactive α3_135-145-specific T cells expand in patients with Goodpasture disease and, in α3_135-145-immunized HLA-DR15 transgenic mice, α3_135-145-specific T cells infiltrate the kidney and mice develop Goodpasture disease. HLA-DR15 and HLA-DR1 exhibit distinct peptide repertoires and binding preferences and present the α3_135-145 epitope in different binding registers. HLA-DR15-α3_135-145 tetramer⁺ T cells in HLA-DR15 transgenic mice exhibit a conventional T-cell phenotype (T_conv) that secretes pro-inflammatory cytokines. In contrast, HLA-DR1-α3_135-145 tetramer⁺ T cells in HLA-DR1 and HLA-DR15/DR1 transgenic mice are predominantly CD4⁺Foxp3⁺ regulatory T cells (T_reg cells) expressing tolerogenic cytokines. HLA-DR1-induced T_reg cells confer resistance to disease in HLA-DR15/DR1 transgenic mice. HLA-DR15⁺ and HLA-DR1⁺ healthy human donors display altered α3_135-145-specific T-cell antigen receptor usage, HLA-DR15-α3_135-145 tetramer⁺ Foxp3^- T_conv and HLA-DR1-α3_135-145 tetramer⁺ Foxp3⁺CD25^hiCD127^lo T_reg dominant phenotypes. Moreover, patients with Goodpasture disease display a clonally expanded α3_135-145-specific CD4⁺ T-cell repertoire. Accordingly, we provide a mechanistic basis for the dominantly protective effect of HLA in autoimmune disease, whereby HLA polymorphism shapes the relative abundance of self-epitope specific T_reg cells that leads to protection or causation of autoimmunity.

MHC-I peptides get out of the groove and enable a novel mechanism of HIV-1 escape

Major histocompatibility complex class I (MHC-I) molecules play a crucial role in immunity by capturing peptides for presentation to T cells and natural killer (NK) cells. The peptide termini are tethered within the MHC-I antigen-binding groove, but it is unknown whether other presentation modes occur. Here we show that 20% of the HLA-B*57:01 peptide repertoire comprises N-terminally extended sets characterized by a common motif at position 1 (P1) to P2. Structures of HLA-B*57:01 presenting N-terminally extended peptides, including the immunodominant HIV-1 Gag epitope TW10 (TSTLQEQIGW), showed that the N terminus protrudes from the peptide-binding groove. The common escape mutant TSNLQEQIGW bound HLA-B*57:01 canonically, adopting a dramatically different conformation than the TW10 peptide. This affected recognition by killer cell immunoglobulin-like receptor (KIR) 3DL1 expressed on NK cells. We thus define a previously uncharacterized feature of the human leukocyte antigen class I (HLA-I) immunopeptidome that has implications for viral immune escape. We further suggest that recognition of the HLA-B*57:01-TW10 epitope is governed by a 'molecular tension' between the adaptive and innate immune systems.

A Molecular Basis for the Presentation of Phosphorylated Peptides by HLA-B Antigens

As aberrant protein phosphorylation is a hallmark of tumor cells, the display of tumor-specific phosphopeptides by Human Leukocyte Antigen (HLA) class I molecules can be exploited in the treatment of cancer by T-cell-based immunotherapy. Yet, the characterization and prediction of HLA-I phospholigands is challenging as the molecular determinants of the presentation of such post-translationally modified peptides are not fully understood. Here, we employed a peptidomic workflow to identify 256 unique phosphorylated ligands associated with HLA-B*40, -B*27, -B*39, or -B*07. Remarkably, these phosphopeptides showed similar molecular features. Besides the specific anchor motifs imposed by the binding groove of each allotype, the predominance of phosphorylation at peptide position 4 (P4) became strikingly evident, as was the enrichment of basic residues at P1. To determine the structural basis of this observation, we carried out a series of peptide binding assays and solved the crystal structures of HLA-B*40 in complex with a phosphorylated ligand or its nonphosphorylated counterpart. Overall, our data provide a clear explanation to the common motif found in the phosphopeptidomes associated to different HLA-B molecules. The high prevalence of phosphorylation at P4 is dictated by the presence of the conserved residue Arg62 in the heavy chain, a structural feature shared by most HLA-B alleles. In contrast, the preference for basic residues at P1 is allotype-dependent and might be linked to the structure of the A pocket. This molecular understanding of the presentation of phosphopeptides by HLA-B molecules provides a base for the improved prediction and identification of phosphorylated neo-antigens, as potentially used for cancer immunotherapy.

Busch D, Peschel C, Rad R, Cox J, Mann M, Krackhardt AM. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry

Although mutations may represent attractive targets for immunotherapy, direct identification of mutated peptide ligands isolated from human leucocyte antigens (HLA) on the surface of native tumour tissue has so far not been successful. Using advanced mass spectrometry (MS) analysis, we survey the melanoma-associated immunopeptidome to a depth of 95,500 patient-presented peptides. We thereby discover a large spectrum of attractive target antigen candidates including cancer testis antigens and phosphopeptides. Most importantly, we identify peptide ligands presented on native tumour tissue samples harbouring somatic mutations. Four of eleven mutated ligands prove to be immunogenic by neoantigen-specific T-cell responses. Moreover, tumour-reactive T cells with specificity for selected neoantigens identified by MS are detected in the patient's tumour and peripheral blood. We conclude that direct identification of mutated peptide ligands from primary tumour material by MS is possible and yields true neoepitopes with high relevance for immunotherapeutic strategies in cancer.

Viral infection causes a shift in the self peptide repertoire presented by human MHC class I molecules

PURPOSE

MHC class I presentation of peptides allows T cells to survey the cytoplasmic protein milieu of host cells. During infection, presentation of self peptides is, in part, replaced by presentation of microbial peptides. However, little is known about the self peptides presented during infection, despite the fact that microbial infections alter host cell gene expression patterns and protein metabolism.

EXPERIMENTAL DESIGN

The self peptide repertoire presented by HLA-A*01;01, HLA-A*02;01, HLA-B*07;02, HLA-B*35;01, and HLA-B*45;01 (where HLA is human leukocyte antigen) was determined by tandem MS before and after vaccinia virus infection.

RESULTS

We observed a profound alteration in the self peptide repertoire with hundreds of self peptides uniquely presented after infection for which we have coined the term "self peptidome shift." The fraction of novel self peptides presented following infection varied for different HLA class I molecules. A large part (approximately 40%) of the self peptidome shift arose from peptides derived from type I interferon-inducible genes, consistent with cellular responses to viral infection. Interestingly, approximately 12% of self peptides presented after infection showed allelic variation when searched against approximately 300 human genomes.

CONCLUSION AND CLINICAL RELEVANCE

Self peptidome shift in a clinical transplant setting could result in alloreactivity by presenting new self peptides in the context of infection-induced inflammation.

Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions

Ag presentation on HLA molecules plays a central role in infectious diseases and tumor immunology. To date, large-scale identification of (neo-)Ags from DNA sequencing data has mainly relied on predictions. In parallel, mass spectrometry analysis of HLA peptidome is increasingly performed to directly detect peptides presented on HLA molecules. In this study, we use a novel unsupervised approach to assign mass spectrometry-based HLA peptidomics data to their cognate HLA molecules. We show that incorporation of deconvoluted HLA peptidomics data in ligand prediction algorithms can improve their accuracy for HLA alleles with few ligands in existing databases. The results of our computational analysis of large datasets of naturally processed HLA peptides, together with experimental validation and protein structure analysis, further reveal how HLA-binding motifs change with peptide length and predict new cooperative effects between distant residues in HLA-B07:02 ligands.

Investigation of the roles of trace elements during hepatitis C virus infection using protein-protein interactions and a shortest path algorithm

BACKGROUND

Hepatitis is a type of infectious disease that induces inflammation of the liver without pinpointing a particular pathogen or pathogenesis. Type C hepatitis, as a type of hepatitis, has been reported to induce cirrhosis and hepatocellular carcinoma within a very short amount of time. It is a great threat to human health. Some studies have revealed that trace elements are associated with infection with and immune rejection against hepatitis C virus (HCV). However, the mechanism underlying this phenomenon is still unclear.

METHODS

In this study, we aimed to expand our knowledge of this phenomenon by designing a computational method to identify genes that may be related to both HCV and trace element metabolic processes. The searching procedure included three stages. First, a shortest path algorithm was applied to a large network, constructed by protein-protein interactions, to identify potential genes of interest. Second, a permutation test was executed to exclude false discoveries. Finally, some rules based on the betweenness and associations between candidate genes and HCV and trace elements were built to select core genes among the remaining genes.

RESULTS

12 lists of genes, corresponding to 12 types of trace elements, were obtained. These genes are deemed to be associated with HCV infection and trace elements metabolism.

CONCLUSIONS

The analyses indicate that some genes may be related to both HCV and trace element metabolic processes, further confirming the associations between HCV and trace elements. The method was further tested on another set of HCV genes, the results indicate that this method is quite robustness.

GENERAL SIGNIFICANCE

The newly found genes may partially reveal unknown mechanisms between HCV infection and trace element metabolism. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Human Leukocyte Antigen (HLA) B27 Allotype-Specific Binding and Candidate Arthritogenic Peptides Revealed through Heuristic Clustering of Data-independent Acquisition Mass Spectrometry (DIA-MS) Data

Expression of HLA-B27 is strongly associated with ankylosing spondylitis (AS) and other spondyloarthropathies. While this is true for the majority of HLA-B27 allotypes, HLA-B*27:06 and HLA-B*27:09 are not associated with AS. These two subtypes contain polymorphisms that are ideally positioned to influence the bound peptide repertoire. The existence of disease-inducing peptides (so-called arthritogenic peptides) has therefore been proposed that are exclusively presented by disease-associated HLA-B27 allotypes. However, we have recently demonstrated that this segregation of allotype-bound peptides is not the case and that many peptides that display sequence features predicted to favor binding to disease-associated subtypes are also capable of being presented naturally by protective alleles. To further probe more subtle quantitative changes in peptide presentation, we have used a combination of data-independent acquisition (DIA) and multiple reaction monitoring (MRM) mass spectrometry to quantify the abundance of 1646 HLA-B27 restricted peptides across the eight most frequent HLA-B27 allotypes (HLA-B*27:02-HLA-B*27:09). We utilized K means cluster analysis to group peptides with similar allelic binding preferences across the eight HLA-B27 allotypes, which enabled us to identify the most-stringent binding characteristics for each HLA-B27 allotype and further refined their existing consensus-binding motifs. Moreover, a thorough analysis of this quantitative dataset led to the identification of 26 peptides, which are presented in lower abundance by HLA-B*27:06 and HLA-B*27:09 compared with disease-associated HLA-B27 subtypes. Although these differences were observed to be very subtle, these 26 peptides might encompass the sought-after arthritogenic peptide(s).

Comparative Analysis of the Endogenous Peptidomes Displayed by HLA-B*27 and Mamu-B*08: Two MHC Class I Alleles Associated with Elite Control of HIV/SIV Infection

Indian rhesus macaques are arguably the most reliable animal models in AIDS research. In this species the MHC class I allele Mamu-B*08, among others, is associated with elite control of SIV replication. A similar scenario is observed in humans where the expression of HLA-B*27 or HLA-B*57 has been linked to slow or no progression to AIDS after HIV infection. Despite having large differences in their primary structure, it has been reported that HLA-B*27 and Mamu-B*08 display peptides with sequence similarity. To fine-map the Mamu-B*08 binding motif and assess its similarities with that of HLA-B*27, we affinity purified the peptidomes bound to these MHC class I molecules and analyzed them by LC-MS, identifying several thousands of endogenous ligands. Sequence analysis of both sets of peptides revealed a degree of similarity in their binding motifs, especially at peptide position 2 (P2), where arginine was present in the vast majority of ligands of both allotypes. In addition, several differences emerged from this analysis: (i) ligands displayed by Mamu-B*08 tended to be shorter and to have lower molecular weight, (ii) Mamu-B*08 showed a higher preference for glutamine at P2 as a suboptimal binding motif, and (iii) the second major anchor position, found at PΩ, was much more restrictive in Mamu-B*08. In this regard, HLA-B*27 bound efficiently peptides with aliphatic, aromatic (including tyrosine), and basic C-terminal residues while Mamu-B*08 preferred peptides with leucine and phenylalanine in this position. Finally, in silico estimations of binding efficiency and competitive binding assays to Mamu-B*08 of several selected peptides revealed a good correlation between the characterized anchor motif and binding affinity. These results deepen our understanding of the molecular basis of the presentation of peptides by Mamu-B*08 and can contribute to the detection of novel SIV epitopes restricted by this allotype.

A Systems Approach to Understand Antigen Presentation and the Immune Response

The mammalian immune system has evolved to respond to pathogenic, environmental, and cellular changes in order to maintain the health of the host. These responses include the comparatively primitive innate immune response, which represents a rapid and relatively nonspecific reaction to challenge by pathogens and the more complex cellular adaptive immune response. This adaptive response evolves with the pathogenic challenge, involves the cross talk of several cell types, and is highly specific to the pathogen due to the liberation of peptide antigens and their presentation on the surface of affected cells. Together these two forms of immunity provide a surveillance mechanism for the system-wide scrutiny of cellular function, environment, and health. As such the immune system is best understood at a systems biology level, and studies that combine gene expression, protein expression, and liberation of peptides for antigen presentation can be combined to provide a detailed understanding of immunity. This chapter details our experience in identifying peptide antigens and combining this information with more traditional proteomics approaches to understand the generation of immune responses on a holistic level.

An open-source computational and data resource to analyze digital maps of immunopeptidomes

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

DOI:http://dx.doi.org/10.7554/eLife.07661.001

The cells of the immune system protect us by recognizing telltale molecules produced by damaged and diseased cells, or by infection-causing microorganisms (which are also called pathogens). To help with this process, the cells in our bodies display small fragments of proteins (called peptides) on their surface that are then checked by the immune cells. Collectively, these peptides are referred to as the ‘immunopeptidome’, and deciphering the complexity of the human immunopeptidome is important for both basic research and medical science. Such an achievement would help to guide the development of next-generation vaccines and therapies against autoimmune disorders, infectious diseases and cancers.

In the past, immune peptides were mostly identified using a technique that is commonly called ‘shotgun’ mass spectrometry. However, this approach doesn't always provide reproducible results. In 2012, researchers reported the development of a new approach—which they called ‘SWATH’ mass spectrometry—that could yield more reproducible data.

Now, Caron et al.—including many of the researchers involved in the 2012 study—have developed a large collection of standardized tests that use SWATH mass spectrometry to analyze the human immunopeptidome. The workflow and the computational and data resources developed as part of this international effort are the first steps toward highly reproducible and measurable analyses of the immunopeptidome across many samples. Moreover, the large repository of assays generated by the project has been made public and will serve a large community of researchers, which should enable better collaborations.

In the future, SWATH mass spectrometry could be used as a robust technology for the reproducible detection and measurement of pathogen-specific or cancer-specific immune peptides. This could greatly help in the design of personalized immune-based therapies.

DOI:http://dx.doi.org/10.7554/eLife.07661.002

Revisiting the arthritogenic peptide theory: quantitative not qualitative changes in the peptide repertoire of HLA-B27 allotypes

OBJECTIVE

The association of HLA-B27 with spondyloarthropathy is one of the strongest documented for any autoimmune disease. A common hypothesis for this association is the arthritogenic peptide concept. This dictates that differences in the peptide binding preferences of disease-associated and non-disease-associated HLA-B27 allotypes underlie the presentation of bacterial and self-peptides, leading to cross-reactive T cell immunity and subsequent autoimmune attack of affected tissues. The aim of this study was to analyze and compare self-peptides from 8 HLA-B27 allotypes, to increase existing data sets of HLA-B27 ligands, to refine and compare their consensus-binding motifs, and to reveal similarities and differences in the peptide repertoire of the HLA-B27 subtypes.

METHODS

Qualitative differences in the peptides bound to the 8 most frequent HLA-B27 subtypes were determined by tandem mass spectrometry, and quantitative changes in allelic binding specificities were determined by highly sensitive and targeted multiple reaction monitoring mass spectrometry.

RESULTS

We identified >7,500 major histocompatibility complex class I peptides derived from the 8 most common HLA-B27 allotypes (HLA-B*27:02 to HLA-B*27:09). We describe individual binding motifs for these alleles for the 9-12-mer ligands. The peptide repertoires of these closely related alleles showed significant overlap. Allelic polymorphisms resulting in changes in the amino acid composition of the antigen-binding cleft manifested largely as quantitative changes in the peptide cargo of these molecules.

CONCLUSION

Absolute binding preferences of HLA-B27 allotypes do not explain disease association. The arthritogenic peptide theory needs to be reassessed in terms of quantitative changes in self-peptide presentation, T cell selection, and altered conformation of bound peptides.