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.

Endogenous H3.3K27M derived peptide restricted to HLA-A∗02:01 is insufficient for immune-targeting in diffuse midline glioma.

Diffuse midline glioma (DMG) is a childhood brain tumor with an extremely poor prognosis. Chimeric antigen receptor (CAR) T cell therapy has recently demonstrated some success in DMG, but there may a need to target multiple tumor-specific targets to avoid antigen escape. We developed a second-generation CAR targeting an HLA-A∗02:01 restricted histone 3K27M epitope in DMG, the target of previous peptide vaccination and T cell receptor-mimics. These CAR T cells demonstrated specific, titratable, binding to cells pulsed with the H3.3K27M peptide. However, we were unable to observe scFv binding, CAR T cell activation, or cytotoxic function against H3.3K27M+ patient-derived models. Despite using sensitive immunopeptidomics, we could not detect the H3.3K27M26-35-HLA-A∗02:01 peptide on these patient-derived models. Interestingly, other non-mutated peptides from DMG were detected bound to HLA-A∗02:01 and other class I molecules, including a novel HLA-A3-restricted peptide encompassing the K27M mutation and overlapping with the H3 K27M26-35-HLA-A∗02:01 peptide. These results suggest that targeting the H3 K27M26-35 mutation in context of HLA-A∗02:01 may not be a feasible immunotherapy strategy because of its lack of presentation. These findings should inform future investigations and clinical trials in DMG.

A combined immunopeptidomics, proteomics, and cell surface proteomics approach to identify immunotherapy targets for diffuse intrinsic pontine glioma.

Introduction: Diffuse intrinsic pontine glioma (DIPG), recently reclassified as a subtype of diffuse midline glioma, is a highly aggressive brainstem tumor affecting children and young adults, with no cure and a median survival of only 9 months. Conventional treatments are ineffective, highlighting the need for alternative therapeutic strategies such as cellular immunotherapy. However, identifying unique and tumor-specific cell surface antigens to target with chimeric antigen receptor (CAR) or T-cell receptor (TCR) therapies is challenging. Methods: In this study, a multi-omics approach was used to interrogate patient-derived DIPG cell lines and to identify potential targets for immunotherapy. Results: Through immunopeptidomics, a range of targetable peptide antigens from cancer testis and tumor-associated antigens as well as peptides derived from human endogenous retroviral elements were identified. Proteomics analysis also revealed upregulation of potential drug targets and cell surface proteins such as Cluster of differentiation 27 (CD276) B7 homolog 3 protein (B7H3), Interleukin 13 alpha receptor 2 (IL-13Rα2), Human Epidermal Growth Factor Receptor 3 (HER2), Ephrin Type-A Receptor 2 (EphA2), and Ephrin Type-A Receptor 3 (EphA3). Discussion: The results of this study provide a valuable resource for the scientific community to accelerate immunotherapeutic approaches for DIPG. Identifying potential targets for CAR and TCR therapies could open up new avenues for treating this devastating disease.

SAPrIm, a semi-automated protocol for mid-throughput immunopeptidomics.

Human leukocyte antigen (HLA) molecules play a crucial role in directing adaptive immune responses based on the nature of their peptide ligands, collectively coined the immunopeptidome. As such, the study of HLA molecules has been of major interest in the development of cancer immunotherapies such as vaccines and T-cell therapies. Hence, a comprehensive understanding and profiling of the immunopeptidome is required to foster the growth of these personalised solutions. We herein describe SAPrIm, an Immunopeptidomics tool for the Mid-Throughput era. This is a semi-automated workflow involving the KingFisher platform to isolate immunopeptidomes using anti-HLA antibodies coupled to a hyper-porous magnetic protein A microbead, a variable window data independent acquisition (DIA) method and the ability to run up to 12 samples in parallel. Using this workflow, we were able to concordantly identify and quantify ~400 - 13000 unique peptides from 5e5 - 5e7 cells, respectively. Overall, we propose that the application of this workflow will be crucial for the future of immunopeptidome profiling, especially for mid-size cohorts and comparative immunopeptidomics studies.

Immunolyser: A web-based computational pipeline for analysing and mining immunopeptidomic data.

Immunopeptidomics has made tremendous contributions to our understanding of antigen processing and presentation, by identifying and quantifying antigenic peptides presented on the cell surface by Major Histocompatibility Complex (MHC) molecules. Large and complex immunopeptidomics datasets can now be routinely generated using Liquid Chromatography-Mass Spectrometry techniques. The analysis of this data - often consisting of multiple replicates/conditions - rarely follows a standard data processing pipeline, hindering the reproducibility and depth of analysis of immunopeptidomic data. Here, we present Immunolyser, an automated pipeline designed to facilitate computational analysis of immunopeptidomic data with a minimal initial setup. Immunolyser brings together routine analyses, including peptide length distribution, peptide motif analysis, sequence clustering, peptide-MHC binding affinity prediction, and source protein analysis. Immunolyser provides a user-friendly and interactive interface via its webserver and is freely available for academic purposes at https://immunolyser.erc.monash.edu/. The open-access source code can be downloaded at our GitHub repository: https://github.com/prmunday/Immunolyser. We anticipate that Immunolyser will serve as a prominent computational pipeline to facilitate effortless and reproducible analysis of immunopeptidomic data.

Benchmarking Bioinformatics Pipelines in Data-Independent Acquisition Mass Spectrometry for Immunopeptidomics.

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.

New insights and approaches for analyses of immunopeptidomes.

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.

Hydroxychloroquine inhibits the mitochondrial antioxidant system in activated T cells.

Although hydroxychloroquine (HCQ) has long been used to treat autoimmune diseases, its mechanism of action remains poorly understood. In CD4 T-cells, we found that a clinically relevant concentration of HCQ inhibited the mitochondrial antioxidant system triggered by TCR crosslinking, leading to increased mitochondrial superoxide, impaired activation-induced autophagic flux, and reduced proliferation of CD4 T-cells. In antigen-presenting cells, HCQ also reduced constitutive activation of the endo-lysosomal protease legumain and toll-like receptor 9, thereby reducing cytokine production, but it had little apparent impact on constitutive antigen processing and peptide presentation. HCQ's effects did not require endo-lysosomal pH change, nor impaired autophagosome-lysosome fusion. We explored the clinical relevance of these findings in patients with celiac disease-a prototypic CD4 T-cell-mediated disease-and found that HCQ limits ex vivo antigen-specific T cell responses. We report a T-cell-intrinsic immunomodulatory effect from HCQ and suggest potential re-purposing of HCQ for celiac disease.

The differentiation state of the Schwann cell progenitor drives phenotypic variation between two contagious cancers.

Contagious cancers are a rare pathogenic phenomenon in which cancer cells gain the ability to spread between genetically distinct hosts. Nine examples have been identified across marine bivalves, dogs and Tasmanian devils, but the Tasmanian devil is the only mammalian species known to have given rise to two distinct lineages of contagious cancer, termed Devil Facial Tumour 1 (DFT1) and 2 (DFT2). Remarkably, DFT1 and DFT2 arose independently from the same cell type, a Schwann cell, and while their ultra-structural features are highly similar they exhibit variation in their mutational signatures and infection dynamics. As such, DFT1 and DFT2 provide a unique framework for investigating how a common progenitor cell can give rise to distinct contagious cancers. Using a proteomics approach, we show that DFT1 and DFT2 are derived from Schwann cells in different differentiation states, with DFT2 carrying a molecular signature of a less well differentiated Schwann cell. Under inflammatory signals DFT1 and DFT2 have different gene expression profiles, most notably involving Schwann cell markers of differentiation, reflecting the influence of their distinct origins. Further, DFT2 cells express immune cell markers typically expressed during nerve repair, consistent with an ability to manipulate their extracellular environment, facilitating the cell's ability to transmit between individuals. The emergence of two contagious cancers in the Tasmanian devil suggests that the inherent plasticity of Schwann cells confers a vulnerability to the formation of contagious cancers.

Immunopeptidogenomics: Harnessing RNA-Seq to Illuminate the Dark Immunopeptidome.

Human leukocyte antigen (HLA) molecules are cell-surface glycoproteins that present peptide antigens on the cell surface for surveillance by T lymphocytes, which contemporaneously seek signs of disease. Mass spectrometric analysis allows us to identify large numbers of these peptides (the immunopeptidome) following affinity purification of solubilized HLA-peptide complexes. However, in recent years, there has been a growing awareness of the "dark side" of the immunopeptidome: unconventional peptide epitopes, including neoepitopes, which elude detection by conventional search methods because their sequences are not present in reference protein databases (DBs). Here, we establish a bioinformatics workflow to aid identification of peptides generated by noncanonical translation of mRNA or by genome variants. The workflow incorporates both standard transcriptomics software and novel computer programs to produce cell line-specific protein DBs based on three-frame translation of the transcriptome. The final protein DB also includes sequences resulting from variants determined by variant calling on the same RNA-Seq data. We then searched our experimental data against both transcriptome-based and standard DBs using PEAKS Studio (Bioinformatics Solutions, Inc). Finally, further novel software helps to compare the various result sets arising for each sample, pinpoint putative genomic origins for unconventional sequences, and highlight potential neoepitopes. We applied the workflow to study the immunopeptidome of the acute myeloid leukemia cell line THP-1, using RNA-Seq and immunopeptidome data. We confidently identified over 14,000 peptides from three replicates of purified HLA peptides derived from THP-1 cells using the conventional UniProt human proteome. Using the transcriptome-based DB generated using our workflow, we recapitulated >85% of these and also identified 1029 unconventional peptides not explained by UniProt, including 16 sequences caused by nonsynonymous variants. Our workflow, which we term "immunopeptidogenomics," can provide DBs, which include pertinent unconventional sequences and allow neoepitope discovery, without becoming too large to search. Immunopeptidogenomics is a step toward unbiased search approaches that are needed to illuminate the dark side of the immunopeptidome.

HLA class II immunopeptidomics reveals that co-inherited HLA-allotypes within an extended haplotype can improve proteome coverage for immunosurveillance.

Human leucocyte antigen (HLA) class II molecules in humans are encoded by three different loci, HLA-DR, -DQ, and -DP. These molecules share approximately 70% sequence similarity and all present peptide ligands to circulating T cells. While the peptide repertoires of numerous HLA-DR, -DQ, and -DP allotypes have been examined, there have been few reports on the combined repertoire of these co-inherited molecules expressed in a single cell as an extended HLA haplotype. Here we describe the endogenous peptide repertoire of a human B lymphoblastoid cell line (C1R) expressing the class II haplotype HLA-DR12/DQ7/DP4. We have identified 71350 unique naturally processed peptides presented collectively by HLA-DR12, HLA-DQ7, or HLA-DP4. The resulting "haplodome" is complemented by the cellular proteome defined by standard LC-MS/MS approaches. This large dataset has shed light on properties of these class II ligands especially the preference for membrane and extracellular source proteins. Our data also provides insights into the co-evolution of these conserved haplotypes of closely linked and co-inherited HLA molecules; which together increase sequence coverage of cellular proteins for immune surveillance with minimal overlap between each co-inherited HLA-class II allomorph.

The self-peptide repertoire plays a critical role in transplant tolerance induction.

While direct allorecognition underpins both solid organ allograft rejection and tolerance induction, the specific molecular targets of most directly alloreactive CD8+ T cells have not been defined. In this study, we used a combination of genetically engineered major histocompatibility complex class I (MHC I) constructs, mice with a hepatocyte-specific mutation in the class I antigen-presentation pathway, and immunopeptidomic analysis to provide definitive evidence for the contribution of the peptide cargo of allogeneic MHC I molecules to transplant tolerance induction. We established a systematic approach for the discovery of directly recognized pMHC epitopes and identified 17 strongly immunogenic H-2Kb-associated peptides recognized by CD8+ T cells from B10.BR (H-2k) mice, 13 of which were also recognized by BALB/c (H-2d) mice. As few as 5 different tetramers used together were able to identify a high proportion of alloreactive T cells within a polyclonal population, suggesting that there are immunodominant allogeneic MHC-peptide complexes that can account for a large component of the alloresponse.

The RNA-Binding Proteins SRP14 and HMGB3 Control HIV-1 Tat mRNA Processing and Translation During HIV-1 Latency.

HIV-1 Tat protein is essential for virus production. RNA-binding proteins that facilitate Tat production may be absent or downregulated in resting CD4+ T-cells, the main reservoir of latent HIV in people with HIV (PWH) on antiretroviral therapy (ART). In this study, we examined the role of Tat RNA-binding proteins on the expression of Tat and control of latent and productive infection. Affinity purification coupled with mass spectrometry analysis was used to detect binding partners of MS2-tagged tat mRNA in a T cell-line model of HIV latency. The effect of knockdown and overexpression of the proteins of interest on Tat transactivation and translation was assessed by luciferase-based reporter assays and infections with a dual color HIV reporter virus. Out of the 243 interactions identified, knockdown of SRP14 (Signal Recognition Particle 14) negatively affected tat mRNA processing and translation as well as Tat-mediated transactivation, which led to an increase in latent infection. On the other hand, knockdown of HMGB3 (High Mobility Group Box 3) resulted in an increase in Tat transactivation and translation as well as an increase in productive infection. Footprinting experiments revealed that SRP14 and HMGB3 proteins bind to TIM-TAM, a conserved RNA sequence-structure in tat mRNA that functions as a Tat IRES modulator of tat mRNA. Overexpression of SRP14 in resting CD4+ T-cells from patients on ART was sufficient to reverse HIV-1 latency and induce virus production. The role of SRP14 and HMGB3 proteins in controlling HIV Tat expression during latency will be further assessed as potential drug targets.

Resourcing, annotating, and analysing synthetic peptides of SARS-CoV-2 for immunopeptidomics and other immunological studies.

SARS-CoV-2 has caused a significant ongoing pandemic worldwide. A number of studies have examined the T cell mediated immune responses against SARS-CoV-2, identifying potential T cell epitopes derived from the SARS-CoV-2 proteome. Such studies will aid in identifying targets for vaccination and immune monitoring. In this study, we applied tandem mass spectrometry and proteomic techniques to a library of ∼40,000 synthetic peptides, in order to generate a large dataset of SARS-CoV-2 derived peptide MS/MS spectra. On this basis, we built an online knowledgebase, termed virusMS (https://virusms.erc.monash.edu/), to document, annotate and analyse these synthetic peptides and their spectral information. VirusMS incorporates a user-friendly interface to facilitate searching, browsing and downloading the database content. Detailed annotations of the peptides, including experimental information, peptide modifications, predicted peptide-HLA (human leukocyte antigen) binding affinities, and peptide MS/MS spectral data, are provided in virusMS.

Isolation of HLA Bound Peptides by Immunoaffinity Capture and Identification by Mass Spectrometry.

This article describes the purification of HLA-bound peptides and their subsequent sequencing by mass spectrometry. These methods can be used for both HLA class I and class II molecules and can be adapted to different species depending on the availability of specific antibodies. Peptides can be successfully isolated from a variety of sample types, including in vitro cultured cells and primary tissues. The method involves the affinity capture of HLA-peptide complexes and separation of peptides from HLA heavy chains, followed by tailored interrogation by mass spectrometry to take into account the non-tryptic nature of endogenously derived HLA-bound peptides. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Preparation of immunoaffinity column Alternate Protocol 1: Preparation of microscale immunoaffinity column Basic Protocol 2: Generation of cell lysate and HLA immunoaffinity purification Alternate Protocol 2: Microscale immunoaffinity purification Basic Protocol 3: Separation of HLA peptides by reverse-phase high-performance liquid chromatography (RP-HPLC) Alternate Protocol 3: Isolation of HLA peptides using molecular weight cutoff (MWCO) filter Basic Protocol 4: Mass spectrometry and data analysis.

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.

Spliced Peptides and Cytokine-Driven Changes in the Immunopeptidome of Melanoma.

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.

HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions.

The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4+ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.