Epstein-Barr Virus (EBV) is closely linked to nasopharyngeal carcinoma (NPC), notably prevalent in southern China. Although type II latency of EBV plays a crucial role in the development of NPC, some lytic genes and intermittent reactivation are also critical for viral propagation and tumor progression. Since T cell-mediated immunity is effective in targeted killing of EBV-positive cells, it is important to identify EBV-derived peptides presented by highly prevalent human leukocyte antigen class I (HLA-I) molecules throughout the EBV life cycle. Here, we constructed an EBV-positive NPC cell model to evaluate the presentation of EBV lytic phase peptides on streptavidin-tagged specific HLA-I molecules. Utilizing a mass spectrometry (LC-MS/MS)-based immunopeptidomic approach, we characterized eleven novel EBV peptides as well as two previously identified peptides. Furthermore, we determined these peptides were immunogenic and could stimulate PBMCs from EBV VCA/NA-IgA positive donors in an NPC endemic southern Chinese population. Overall, this work demonstrates that highly prevalent HLA-I-specific EBV peptides can be captured and functionally presented to elicit immune responses in an in vitro model, which provides insight into the epitopes presented during EBV lytic cycle and reactivation. It expands the range of viral targets for potential NPC early diagnosis and treatment.

Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

Neo-antigens expressed on tumors are targets for development of cancer immunotherapy strategies. Use of prediction algorithms to identify neo-antigens yields a significant number of peptides that must be validated in laborious and time-consuming methods; many prove to be false-positive identifications. The use of HLA peptidomics allows the isolation of the HLA-peptide complexes directly from cells and can be done on fresh tumor, patient-derived xerographs, or cell lines when the tissue sample is limited. This method can be used to identify both HLA class I and HLA class II or any different MHC from different species. Here we describe the steps to create the immune-affinity columns used from the process, the immunoprecipitation procedure, and also the isolation of the peptides that will be analyzed by mass spectrometry.

HLA-I molecules play a central role in antigen presentation. They typically bind 9- to 12-mer peptides, and their canonical binding mode involves anchor residues at the second and last positions of their ligands. To investigate potential noncanonical binding modes, we collected in-depth and accurate HLA peptidomics datasets covering 54 HLA-I alleles and developed algorithms to analyze these data. Our results reveal frequent (442 unique peptides) and statistically significant C-terminal extensions for at least eight alleles, including the common HLA-A03:01, HLA-A31:01, and HLA-A68:01. High resolution crystal structure of HLA-A68:01 with such a ligand uncovers structural changes taking place to accommodate C-terminal extensions and helps unraveling sequence and structural properties predictive of the presence of these extensions. Scanning viral proteomes with the C-terminal extension motifs identifies many putative epitopes and we demonstrate direct recognition by human CD8+ T cells of a 10-mer epitope from cytomegalovirus predicted to follow the C-terminal extension binding mode.

The interaction between HLA class II peptide complexes on antigen-presenting cells and CD4+ T cells is of fundamental importance for anticancer and antipathogen immunity as well as for the maintenance of immunological tolerance. To study CD4+ T cell reactivities, detailed knowledge of the presented peptides is necessary. In recent years, dramatic advances in the characterization of membranal and soluble HLA class I peptidomes could be observed. However, the same is not true for HLA class II peptidomes, where only few studies identify more than hundred peptides. Here we describe a MS-based workflow for the characterization of membranal and soluble HLA class II DR and DQ peptidomes. Using this workflow, we identify a total of 8595 and 3727 HLA class II peptides from Maver-1 and DOHH2 cells, respectively. Based on this data, a motif-based binding predictor is developed and compared to NetMHCIIpan 3.1. We then apply the workflow to human plasma, resulting in the identification of between 34 and 152 HLA-DR and between 100 and 180 HLA-DQ peptides, respectively. Finally, we implement a data-independent acquisition workflow to increase reproducibility and sensitivity of HLA class II peptidome characterizations.

The characterization of peptides presented by human leukocyte antigen (HLA) class I molecules is crucial for understanding immune processes, biomarker discovery, and the development of novel immunotherapies or vaccines. Mass spectrometry allows the direct identification of thousands of HLA-bound peptides from cell lines, blood, or tissue. In recent years, data-independent acquisition (DIA) mass spectrometry methods have evolved, promising to increase reproducibility and sensitivity over classical data-dependent acquisition (DDA) workflows. Here, we describe a DIA setup on the Q Exactive mass spectrometer, optimized regarding the unique properties of HLA class I peptides. The methodology enables sensitive and highly reproducible characterization of HLA peptidomes from individual cell lines. From up to 16 DDA analyses of 100 million human cells, more than 10 000 peptides could be confidently identified, serving as basis for the generation of spectral libraries. This knowledge enabled the subsequent interrogation of DIA data, leading to the identification of peptide sets with >90% overlap between replicate samples, a prerequisite for the comparative study of closely related specimens. Furthermore, >3000 peptides could be identified from just one million cells after DIA analysis using a library generated from 300 million cells. The reduction in sample quantity and the high reproducibility of DIA-based HLA peptidome analysis should facilitate personalized medicine applications.

Soluble human leukocyte antigen class I (sHLA)-peptide complexes have been suggested to play a role in the modulation of immune responses and in immune evasion of cancer cells. The set of peptides eluted from sHLA molecules could serve as biomarker for the monitoring of patients with cancer or other conditions. Here, we describe an improved sHLA peptidomics methodology resulting in the identification of 1816 to 2761 unique peptide sequences from triplicate analyses of serum or plasma taken from three healthy donors. More than 90% of the identified peptides were 8-11mers and 74% of these sequences were predicted to bind to cognate HLA alleles, confirming the quality of the resulting immunopeptidomes. In comparison to the HLA peptidome of cultured cells, the plasma-derived peptides were predicted to have a higher stability in complex with the cognate HLA molecules and mainly derived from proteins of the plasma membrane or from the extracellular space. The sHLA peptidomes can efficiently be characterized by using the new methodology, thus serving as potential source of biomarkers in various pathological conditions.