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Table S1

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Antigen recognition by CD8+ T cells is governed by the pool of peptide antigens presented on the cell surface in the context of HLA class I complexes. Studies have shown not only a high degree of plasticity in the immunopeptidome, but also that a considerable fraction of all presented peptides is generated through proteasome-mediated splicing of noncontiguous regions of proteins to form novel peptide antigens. Here, we used high-resolution mass spectrometry combined with new bioinformatic approaches to characterize the immunopeptidome of melanoma cells in the presence or absence of IFNγ. In total, we identified more than 60,000 peptides from a single patient-derived cell line (LM-MEL-44) and demonstrated that IFNγ induced changes in the peptidome, with an overlap of only approximately 50% between basal and treated cells. Around 6% to 8% of the peptides were identified as cis-spliced peptides, and 2,213 peptides (1,827 linear and 386 cis-spliced peptides) were derived from known melanoma-associated antigens. These peptide antigens were equally distributed between the constitutive- and IFNγ-induced peptidome. We next examined additional HLA-matched patient-derived cell lines to investigate how frequently these peptides were identified and found that a high proportion of both linear and spliced peptides was conserved between individual patient tumors, drawing on data amassing to more than 100,000 peptide sequences. Several of these peptides showed in vitro immunogenicity across multiple patients with melanoma. These observations highlight the breadth and complexity of the repertoire of immunogenic peptides that can be exploited therapeutically and suggest that spliced peptides are a major class of tumor antigens.

SUMMARYCancer immunotherapies have greatly improved the treatment of several cancers, albeit a large fraction of patients still does not benefit from such treatments. Personalized therapies are envisioned to lead to improved outcomes, yet they have largely been leveraging neoepitopes arising from missense somatic mutations, and as such primarily patients with relatively high tumor mutational burden (TMB) are suitable. With the goal to enhance and broaden its application, we present a pipeline, ObsERV, for the design of personalized cancer immunotherapies based on epitopes derived from endogenous retroviral elements (EVEs). We show that EVE-derived peptides are presented as antigens on tumors and can be predicted by ObsERV. Preclinical testing of ObsERV demonstrates induction of durable poly-functional CD4+ and CD8+ T-cell responses as well as long-term tumor protection. We find signs that the EVE antigen burden may stratify low-TMB cancer patients into groups of differential overall survival, indicating that EVEs serve as a additional tumor antigen source. Furthermore, we find through gene set enrichment analysis, that the expression of Myc targets is correlated with the EVE burden for low-TMB patients indicating that Myc may regulate the expression of epigenetically primed EVEs. Finally, we demonstrate in a pan-cancer analysis that the EVE antigen burden does not correlate with TMB and appears to be cancer type specific. As such, EVEs may facilitate and improve the design of personalized immunotherapy especially for low-TMB patients.

Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex [human leukocyte antigen (HLA) in humans]. These HLA–peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.

The precise calculation of GRACE and GRACE-FO satellite orbits is reliant on knowledge of accurate non-gravitational accelerations acting on the spacecraft. These are measured by the on-board accelerometers that require a thermal environment stabilised to ∼ ± 0.1 °C per revolution. However, during periods of the GRACE mission with reduced thermal control, internal temperature variations reached up to 10 °C within a revolution, causing low-frequency and non-linear drifts in the accelerometer observations. Additionally, accelerometer bias drifts occurred throughout the GRACE mission as changes in the orientation of the orbital plane with respect to the Earth-to-Sun vector caused the satellites to absorb more or less solar energy. These temperature-induced drifts degrade the quality of mass change estimates, particularly during the latter half of the GRACE mission after thermal control of the satellites was terminated. We filter (in the frequency domain) the accelerometer observations to remove these low-frequency components ( f 0.045 mHz). The bias drift removed from the cross-track is then scaled to derive a thermally-based correction for the highly sensitive along-track observations. We then estimate temporal gravity fields using the ANU GRACE software, our filtered accelerometer observations and the range acceleration as the inter-satellite observation. The use of our thermally-corrected accelerometer measurements significantly improves the accuracy of both orbit modelling and gravity field estimation.

HLA-DQ8, a genetic risk factor in type I diabetes (T1D), presents hybrid insulin peptides (HIPs) to autoreactive CD4+ T cells. The abundance of spliced peptides binding to HLA-DQ8 and how they are subsequently recognised by the autoreactive T cell repertoire is unknown. Here we report, the HIP (GQVELGGGNAVEVLK), derived from splicing of insulin and islet amyloid polypeptides, generates a preferred peptide-binding motif for HLA-DQ8. HLA-DQ8-HIP tetramer+ T cells from the peripheral blood of a T1D patient are characterised by repeated TRBV5 usage, which matches the TCR bias of CD4+ T cells reactive to the HIP peptide isolated from the pancreatic islets of a patient with T1D. The crystal structure of three TRBV5+ TCR-HLA-DQ8-HIP complexes shows that the TRBV5-encoded TCR β-chain forms a common landing pad on the HLA-DQ8 molecule. The N- and C-termini of the HIP is recognised predominantly by the TCR α-chain and TCR β-chain, respectively, in all three TCR ternary complexes. Accordingly, TRBV5 + TCR recognition of HIP peptides might occur via a ‘polarised’ mechanism, whereby each chain within the αβTCR heterodimer recognises distinct origins of the spliced peptide presented by HLA-DQ8., Epitopes formed by fusion of more than one self peptide, such as proinsulin and other β cell proteins, can result in the formation of non-self hybrid peptides that can potentially trigger autoimmune responses. Here the authors show how TRBV5 + T cell receptors are geared towards recognition of HLA-DQ8 bound hybrid peptides in patients with type 1 diabetes.

Here, we present a protocol to identify immunogenic self-peptide/allogeneic major histocompatibility complex (MHC) epitopes. We describe the generation of enriched alloreactive CD8+ T cells by priming mice with a skin graft expressing the allogeneic MHC class I molecule of interest, followed by boosting with a liver-specific AAV vector encoding the heavy chain of that donor MHC allomorph. We then use a peptide-exchange approach to assemble a range of peptide-MHC (pMHC) multimers for measuring recognition of the various epitopes by these alloreactive T cells. For complete details on the use and execution of this protocol, please refer to Son et al. (2021).

The Major Histocompatibility Complex class I-related protein 1 (MR1) presents small molecule metabolites, drugs, and drug-like molecules that are recognized by MR1-reactive T cells. While we have an understanding of how antigens bind to MR1 and upregulate MR1 cell surface expression, a quantitative, cell-free, assessment of MR1 ligand-binding affinity was lacking. Here, we developed a fluorescence polarization-based assay in which fluorescent MR1 ligand was loaded into MR1 protein in vitro and competitively displaced by candidate ligands over a range of concentrations. Using this assay, ligand affinity for MR1 could be differentiated as strong (IC

The p53 protein is mutated in more than 50% of human cancers. Mutated p53 proteins not only lose their normal function but often acquire novel oncogenic functions, a phenomenon termed mutant p53 gain-of-function. Mutant p53 has been shown to affect the transcription of a range of genes, as well as protein–protein interactions with transcription factors and other effectors; however, no one has intensively investigated and identified these proteins, or their MHC presented epitopes, from the viewpoint of their ability to act as targets for immunotherapeutic interventions. We investigated the molecular changes that occurred after the TP53 null osteosarcoma cells, SaOS-2, were transfected with one of two conformational p53-mutants, either R175H or R273H. We then examined the phenotypic and functional changes using macroscopic observations, proliferation, gene expression and proteomics alongside immunopeptidome profiling of peptide antigen presentation in the context of major histocompatibility complex (MHC) class I molecules. We identified several candidate proteins in both TP53 mutant cell lines with differential expression when compared to the TP53 null vector control, SaOS-V. Quantitative SWATH proteomics combined with immune-peptidome analysis of the class-I eluted peptides identified several epitopes presented on pMHC and in silico analysis shortlisted which antigens were expressed in a range of cancerous but not adjacent healthy tissues. Out of all the candidates, KLC1 and TOP2A showed high levels of expression in every tumor type examined. From these proteins, three A2 and four pan HLA-A epitopes were identified in both R175H and R273H from TOP2A. We have now provided a short list of future immunotherapy targets for the treatment of cancers harboring mutated TP53.

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.

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein–protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography–MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics. ISSN:1548-7105 ISSN:1548-7091

Past sea-level change represents the large-scale state of global climate, reflecting the waxing and waning of global ice sheets and the corresponding effect on ocean volume. Recent developments in sampling and analytical methods enable us to more precisely reconstruct past sea-level changes using geological indicators dated by radiometric methods. However, ice-volume changes alone cannot wholly account for these observations of local, relative sea-level change because of various geophysical factors including glacio-hydro-isostatic adjustments (GIA). The mechanisms behind GIA cannot be ignored when reconstructing global ice volume, yet they remain poorly understood within the general sea-level community. In this paper, various geophysical factors affecting sea-level observations are discussed and the details and impacts of these processes on estimates of past ice volumes are introduced.

Summary Peptides that bind to and are presented on the cell surface by human leucocyte antigen (HLA) molecules play a critical role in adaptive immunity. For a long time it was believed that all the HLA-bound peptides were generated through simple proteolysis of linear sequences of cellular proteins, and therefore are templated in the genome and proteome. However, evidence for untemplated peptide ligands of HLA molecules has accumulated during the last two decades, with a recent global analysis of HLA-bound peptides suggesting that a considerable proportion of HLA-bound peptides are potentially generated through splicing/fusion of discontinuous peptide segments from one or two distinct proteins. In this review, we will evaluate recent discoveries and debates on the contribution of spliced peptides to the HLA class I immunopeptidome, consider biochemical rules for splicing and the potential role of these spliced peptides in immune recognition.

Thorough understanding of the role of CD4 T cells in immunity can be greatly assisted by the study of responses to defined specificities. This requires knowledge of Plasmodium-derived immunogenic epitopes, of which only a few have been identified, especially for the mouse C57BL/6 background. We recently developed a TCR transgenic mouse line, termed PbT-II, that produces CD4+ T cells specific for an MHC class II (I-Ab)-restricted Plasmodium epitope and is responsive to both sporozoites and blood-stage P. berghei. Here, we identify a peptide within the P. berghei heat shock protein 90 as the cognate epitope recognised by PbT-II cells. We show that C57BL/6 mice infected with P. berghei blood-stage induce an endogenous CD4 T cell response specific for this epitope, indicating cells of similar specificity to PbT-II cells are present in the naive repertoire. Adoptive transfer of in vitro activated TH1-, or particularly TH2-polarised PbT-II cells improved control of P. berghei parasitemia in C57BL/6 mice and drastically reduced the onset of experimental cerebral malaria. Our results identify a versatile, potentially protective MHC-II restricted epitope useful for exploration of CD4 T cell-mediated immunity and vaccination strategies against malaria.

Volcano and other analytical plots (e.g., correlation plots, upset plots, and heatmaps) serve as important data visualization methods for transcriptomic and proteomic analyses. Customizable generation of these plots is fundamentally important for a better understanding of dysregulated expression data and is therefore instrumental for the ensuing pathway analysis and biomarker identification. Here, we present an R-based Shiny application, termed ggVolcanoR, to allow for customizable generation and visualization of volcano plots, correlation plots, upset plots, and heatmaps for differential expression datasets, via a user-friendly interactive interface in both local executable version and web-based application without requiring programming expertise. Compared to currently existing packages, ggVolcanoR offers more practical options to optimize the generation of publication-quality volcano and other analytical plots for analyzing and comparing dysregulated genes/proteins across multiple differential expression datasets. In addition, ggVolcanoR provides an option to download the customized list of the filtered dysregulated expression data, which can be directly used as input for downstream pathway analysis. The source code of ggVolcanoR is available at https://github.com/KerryAM-R/ggVolcanoR and the webserver of ggVolcanoR 1.0 has been deployed and is freely available for academic purposes at https://ggvolcanor.erc.monash.edu/.

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., Thermostability of the peptide-MHC interaction is important for immunogenicity. Here the authors present a mass spectrometry method to measure thermostability among thousands of peptide-MHC complexes in parallel and a trained artificial neural network to predict immunogenenicity of cancer antigens.

Human leucocyte antigen (HLA) molecules play a key role in health and disease by presenting antigen to T-lymphocytes for immunosurveillance. Immunopeptidomics involves the study of the collection of peptides presented within the antigen-binding groove of HLA molecules. Identifying their nature and diversity is crucial to understanding immunosurveillance especially during infection or for the recognition and potential eradication of tumours. This review discusses recent advances in the isolation, identification, and quantitation of these peptide antigens. New informatics approaches and databases have shed light on the extent of peptide antigens derived from unconventional sources including peptides derived from transcripts associated with frame shifts, long noncoding RNA, incorrectly annotated untranslated regions, post-translational modifications, and proteasomal splicing. Several challenges remain in successful analysis of immunopeptides, yet recent developments point to unexplored biology waiting to be unravelled.

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes—or macromolecular machines—are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

The presentation of pathogen-derived peptides on MHC class I molecules is essential for the initiation of adaptive CD8+ T cell immunity, which in turn is critical for effective control of many significant human infections. The identification of immunogenic pathogen-derived epitopes and a detailed understanding of how they are recognized by TCRs is essential for the design of effective T cell–based vaccines. In this study, we have characterized the T cell recognition and immune responses in mice to two naturally presented influenza A virus–derived peptides previously identified from virally infected cells via mass spectrometry. These neuraminidase-derived peptides, NA181–190 (SGPDNGAVAV) and NA181–191 (SGPDNGAVAVL), are completely overlapping with the exception of a 1 aa extension at the C terminus of the longer peptide. This minor peptidic difference results in the induction of two completely independent and non–cross-reactive T cell populations that show distinct functional characteristics after influenza A virus infection of B6 mice. We show that the unique TCR reactivity to the overlapping peptides is present in the naive repertoire prior to immune expansion in B6 mice. Moreover, we provide a structural explanation underlying the distinct CD8+ T cell reactivities, which reinforces the concept that peptide length is a key determinant of Ag specificity in CD8+ T cell responses.

Penicillin refers to a group of beta-lactam antibiotics that are the first-line treatment for a range of infections. However, they also possess the ability to form novel antigens, or neoantigens, through haptenation of proteins and can stimulate a range of immune-mediated adverse reactions-collectively known as drug hypersensitivity reactions (DHRs). IgE-mediated reactions towards these neoantigens are well studied; however, IgE-independent reactions are less well understood. These reactions usually manifest in a delayed manner as different forms of cutaneous eruptions or liver injury consistent with priming of an immune response. Ex vivo studies have confirmed the infiltration of T cells into the site of inflammation, and the subsets of T cells involved appear dependent on the nature of the reaction. Here, we review the evidence that has led to our current understanding of these immune-mediated reactions, discussing the nature of the lesional T cells, the characterization of drug-responsive T cells isolated from patient blood, and the potential mechanisms by which penicillins enter the antigen processing and presentation pathway to stimulate these deleterious responses. Thus, we highlight the need for a more comprehensive understanding of the underlying genetic and molecular basis of penicillin-induced DHRs.

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.

The identification of T cell epitopes derived from tumour specific antigens remains a significant challenge for the development of peptide-based vaccines and immunotherapies. The use of mass spectrometry-based approaches (immunopeptidomics) can provide powerful new avenues for the identification of such epitopes. In this study we report the use of complementary peptide antigen enrichment methods and a comprehensive mass spectrometric acquisition strategy to provide in-depth immunopeptidome data for the THP-1 cell line, a cell line used widely as a model of human leukaemia. To accomplish this, we combined robust experimental workflows that incorporated ultrafiltration or off-line reversed phase chromatography to enrich peptide ligand as well as a multifaceted data acquisition strategy using an Orbitrap Fusion LC-MS instrument. Using the combined datasets from the two ligand enrichment methods we gained significant depth in immunopeptidome coverage by identifying a total of 41,816 HLA class I peptides from THP-1 cells, including a significant number of peptides derived from different oncogenes or over expressed proteins associated with cancer. The physicochemical properties of the HLA-bound peptides dictated their recovery using the two ligand enrichment approaches and their distribution across the different precursor charge states considered in the data acquisition strategy. The data highlight the complementarity of the two enrichment procedures, and in cases where sample is not limiting, suggest that the combination of both approaches will yield the most comprehensive immunopeptidome information.

Micropolymorphisms within human leukocyte antigen (HLA) class I molecules can change the architecture of the peptide-binding cleft, leading to differences in peptide presentation and T cell recognition. The impact of such HLA variation on natural killer (NK) cell recognition remains unclear. Given the differential association of HLA-B*57:01 and HLA-B*57:03 with the control of HIV, recognition of these HLA-B57 allomorphs by the killer cell immunoglobulin-like receptor (KIR) 3DL1 was compared. Despite differing by only two polymorphic residues, both buried within the peptide-binding cleft, HLA-B*57:01 more potently inhibited NK cell activation. Direct-binding studies showed KIR3DL1 to preferentially recognize HLA-B*57:01, particularly when presenting peptides with positively charged position (P)Ω-2 residues. In HLA-B*57:01, charged PΩ-2 residues were oriented toward the peptide-binding cleft and away from KIR3DL1. In HLA-B*57:03, the charged PΩ-2 residues protruded out from the cleft and directly impacted KIR3DL1 engagement. Accordingly, KIR3DL1 recognition of HLA class I ligands is modulated by both the peptide sequence and conformation, as determined by the HLA polymorphic framework, providing a rationale for understanding differences in clinical associations.