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.

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.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules.

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Direct Priming of CD8+ T Cells Persists in the Face of Cowpox Virus Inhibitors of Antigen Presentation.

Vaccinia virus (VACV) was the vaccine used to eradicate smallpox and is being repurposed as a vaccine vector. CD8+ T cells are key anti-viral mediators, but require priming to become effector or memory cells. Priming requires an interaction with dendritic cells that are either infected (direct priming), or that have acquired virus proteins but remain uninfected (cross priming). To investigate CD8+ T cell priming pathways for VACV, we engineered the virus to express CPXV12 and CPXV203, two inhibitors of antigen presentation encoded by cowpox virus. These intracellular proteins would be expected to block direct but not cross priming. The inhibitors had diverse impacts on the size of anti-VACV CD8+ T cell responses across epitopes and by different infection routes in mice, superficially suggesting variable use of direct and cross priming. However, when we then tested a form of antigen that requires direct priming, we found surprisingly that CD8+ T cell responses were not diminished by co-expression with CPXV12 and CPXV203. We then directly quantified the impact of CPXV12 and CPXV203 on viral antigen presentation using mass spectrometry, which revealed strong, but incomplete inhibition of antigen presentation by the CPXV proteins. Therefore, direct priming of CD8+ T cells by poxviruses is robust enough to withstand highly potent viral inhibitors of antigen presentation. This is a reminder of the limits of viral immune evasion and shows that viral inhibitors of antigen presentation cannot be assumed to dissect cleanly direct and cross priming of anti-viral CD8+ T cells.ImportanceCD8+ T cells are key to anti-viral immunity, so it is important to understand how they are activated. Many viruses have proteins that protect infected cells from T cell attack by interfering with the process that allows virus infection to be recognised by CD8+ T cells. It is thought that these proteins would also stop infected cells from activating T cells in the first place. However, we show here that this is not the case for two very powerful inhibitory proteins from cowpox virus. This demonstrates the flexibility and robustness of immune processes that turn on the immune responses required to fight infection.

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

Overlapping Peptides Elicit Distinct CD8+ T Cell Responses following Influenza A Virus Infection.

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.

Peptide Presentation to T Cells: Solving the Immunogenic Puzzle: Systems Immunology Profiling of Antigen Presentation for Prediction of CD8+ T Cell Immunogenicity.

The vertebrate immune system uses an impressive arsenal of mechanisms to combat harmful cellular states such as infection. One way is via cells delivering real-time snapshots of their protein content to the cell surface in the form of short peptides. Specialized immune cells (T cells) sample these peptides and assess whether they are foreign, warranting an action such as destruction of the infected cell. The delivery of peptides to the cell surface is termed antigen processing and presentation, and decades of research have provided unprecedented understanding of this process. However, predicting the capacity for a given peptide to be immunogenic-to elicit a T cell response-has remained both enigmatic and a long sought-after goal. In the era of big data, a point is being approached where the steps of antigen processing and presentation can be quantified and assessed against peptide immunogenicity in order to build predictive models. This review presents new findings in this area and contemplates challenges ahead.

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.

Most viral peptides displayed by class I MHC on infected cells are immunogenic.

CD8+ T cells are essential effectors in antiviral immunity, recognizing short virus-derived peptides presented by MHC class I (pMHCI) on the surface of infected cells. However, the fraction of viral pMHCI on infected cells that are immunogenic has not been shown for any virus. To approach this fundamental question, we used peptide sequencing by high-resolution mass spectrometry to identify more than 170 vaccinia virus pMHCI presented on infected mouse cells. Next, we screened each peptide for immunogenicity in multiple virus-infected mice, revealing a wide range of immunogenicities. A surprisingly high fraction (>80%) of pMHCI were immunogenic in at least one infected mouse, and nearly 40% were immunogenic across more than half of the mice screened. The high number of peptides found to be immunogenic and the distribution of responses across mice give us insight into the specificity of antiviral CD8+ T cell responses.

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.

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Employing proteomics in the study of antigen presentation: an update.

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

Characterization of the Antigen Processing Machinery and Endogenous Peptide Presentation of a Bat MHC Class I Molecule.

Bats are a major reservoir of emerging and re-emerging infectious diseases, including severe acute respiratory syndrome-like coronaviruses, henipaviruses, and Ebola virus. Although highly pathogenic to their spillover hosts, bats harbor these viruses, and a large number of other viruses, with little or no clinical signs of disease. How bats asymptomatically coexist with these viruses is unknown. In particular, little is known about bat adaptive immunity, and the presence of functional MHC molecules is mostly inferred from recently described genomes. In this study, we used an affinity purification/mass spectrometry approach to demonstrate that a bat MHC class I molecule, Ptal-N*01:01, binds antigenic peptides and associates with peptide-loading complex components. We identified several bat MHC class I-binding partners, including calnexin, calreticulin, protein disulfide isomerase A3, tapasin, TAP1, and TAP2. Additionally, endogenous peptide ligands isolated from Ptal-N*01:01 displayed a relatively broad length distribution and an unusual preference for a C-terminal proline residue. Finally, we demonstrate that this preference for C-terminal proline residues was observed in Hendra virus-derived peptides presented by Ptal-N*01:01 on the surface of infected cells. To our knowledge, this is the first study to identify endogenous and viral MHC class I ligands for any bat species and, as such, provides an important avenue for monitoring and development of vaccines against major bat-borne viruses both in the reservoir and spillover hosts. Additionally, it will provide a foundation to understand the role of adaptive immunity in bat antiviral responses.

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).

T Cell Cross-Reactivity between a Highly Immunogenic EBV Epitope and a Self-Peptide Naturally Presented by HLA-B*18:01+ Cells.

T cell cross-reactivity underpins the molecular mimicry hypothesis in which microbial peptides sharing structural features with host peptides stimulate T cells that cross-react with self-peptides, thereby initiating and/or perpetuating autoimmune disease. EBV represents a potentially important factor in the pathogenesis of several T cell-mediated autoimmune disorders, with molecular mimicry a likely mechanism. In this study, we describe a human self-peptide (DELEIKAY) that is a homolog of a highly immunogenic EBV T cell epitope (SELEIKRY) presented by HLA-B*18:01. This self-peptide was shown to bind stably to HLA-B*18:01, and peptide elution/mass spectrometric studies showed it is naturally presented by this HLA molecule on the surface of human cells. A significant proportion of CD8(+) T cells raised from some healthy individuals against this EBV epitope cross-reacted with the self-peptide. A diverse array of TCRs was expressed by the cross-reactive T cells, with variable functional avidity for the self-peptide, including some T cells that appeared to avoid autoreactivity by a narrow margin, with only 10-fold more of the self-peptide required for equivalent activation as compared with the EBV peptide. Structural studies revealed that the self-peptide-HLA-B*18:01 complex is a structural mimic of the EBV peptide-HLA-B*18:01 complex, and that the strong antiviral T cell response is primarily dependent on the alanine/arginine mismatch at position 7. To our knowledge, this is the first report confirming the natural presentation of a self-peptide cross-recognized in the context of self-HLA by EBV-reactive CD8(+) T cells. These results illustrate how aberrant immune responses and immunopathological diseases could be generated by EBV infection.

Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry.

The generation of antigen-specific reagents is a significant bottleneck in the study of complex pathogens that express many hundreds to thousands of different proteins or to emerging or new strains of viruses that display potential pandemic qualities and therefore require rapid investigation. In these instances the development of antibodies for example can be prohibitively expensive to cover the full pathogen proteome, or the lead time may be unacceptably long in urgent cases where new highly pathogenic viral strains may emerge. Because genomic information on such pathogens can be rapidly acquired this opens up avenues using mass spectrometric approaches to study pathogen antigen expression, host responses and for screening the utility of therapeutics. In particular, data-independent acquisition (DIA) modalities on high-resolution mass spectrometers generate spectral information on all components of a complex sample providing depth of coverage hitherto only seen in genomic deep sequencing. The spectral information generated by DIA can be iteratively interrogated for potentially any protein of interest providing both evidence of protein expression and quantitation. Here we apply a solely DIA mass spectrometry based methodology to profile the viral antigen expression in cells infected with vaccinia virus up to 9 h post infection without the need for antigen specific antibodies or other reagents. We demonstrate deep coverage of the vaccinia virus proteome using a SWATH-MS acquisition approach, extracting quantitative kinetics of 100 virus proteins within a single experiment. The results highlight the complexity of vaccinia protein expression, complementing what is known at the transcriptomic level, and provide a valuable resource and technique for future studies of viral infection and replication kinetics. Furthermore, they highlight the utility of DIA and mass spectrometry in the dissection of host-pathogen interactions.

mRNA Structural constraints on EBNA1 synthesis impact on in vivo antigen presentation and early priming of CD8+ T cells.

Recent studies have shown that virally encoded mRNA sequences of genome maintenance proteins from herpesviruses contain clusters of unusual structural elements, G-quadruplexes, which modulate viral protein synthesis. Destabilization of these G-quadruplexes can override the inhibitory effect on self-synthesis of these proteins. Here we show that the purine-rich repetitive mRNA sequence of Epstein-Barr virus encoded nuclear antigen 1 (EBNA1) comprising G-quadruplex structures, limits both the presentation of MHC class I-restricted CD8(+) T cell epitopes by CD11c(+) dendritic cells in draining lymph nodes and early priming of antigen-specific CD8(+) T-cells. Destabilization of the G-quadruplex structures through codon-modification significantly enhanced in vivo antigen presentation and activation of virus-specific T cells. Ex vivo imaging of draining lymph nodes by confocal microscopy revealed enhanced antigen-specific T-cell trafficking and APC-CD8(+) T-cell interactions in mice primed with viral vectors encoding a codon-modified EBNA1 protein. More importantly, these antigen-specific T cells displayed enhanced expression of the T-box transcription factor and superior polyfunctionality consistent with the qualitative impact of translation efficiency. These results provide an important insight into how viruses exploit mRNA structure to down regulate synthesis of their viral maintenance proteins and delay priming of antigen-specific T cells, thereby establishing a successful latent infection in vivo. Furthermore, targeting EBNA1 mRNA rather than protein by small molecules or antisense oligonucleotides will enhance EBNA1 synthesis and the early priming of effector T cells, to establish a more rapid immune response and prevent persistent infection.

Structural and functional correlates of enhanced antiviral immunity generated by heteroclitic CD8 T cell epitopes.

Peptides that bind poorly to MHC class I molecules often elicit low-functional avidity T cell responses. Peptide modification by altering the anchor residue facilitates increased binding affinity and may elicit T cells with increased functional avidity toward the native epitope ("heteroclitic"). This augmented MHC binding is likely to increase the half-life and surface density of the heteroclitic complex, but precisely how this enhanced T cell response occurs in vivo is not known. Furthermore, the ideal heteroclitic epitope will elicit T cell responses that completely cross-react with the native epitope, maximizing protection and minimizing undesirable off-target effects. Such epitopes have been difficult to identify. In this study, using mice infected with a murine coronavirus that encodes epitopes that elicit high (S510, CSLWNGPHL)- and low (S598, RCQIFANI)-functional avidity responses, we show that increased expression of peptide S598 but not S510 generated T cells with enhanced functional avidity. Thus, immune responses can be augmented toward T cell epitopes with low functional avidity by increasing Ag density. We also identified a heteroclitic epitope (RCVIFANI) that elicited a T cell response with nearly complete cross-reactivity with native epitope and demonstrated increased MHC/peptide abundance compared with native S598. Structural and thermal melt analyses indicated that the Q600V substitution enhanced stability of the peptide/MHC complex without greatly altering the antigenic surface, resulting in highly cross-reactive T cell responses. Our data highlight that increased peptide/MHC complex display contributes to heteroclitic epitope efficacy and describe parameters for maximizing immune responses that cross-react with the native epitope.

Discovery of novel disease-specific and membrane-associated candidate markers in a mouse model of multiple sclerosis.

Multiple sclerosis is a chronic demyelinating disorder characterized by the infiltration of auto-reactive immune cells from the periphery into the central nervous system resulting in axonal injury and neuronal cell death. Experimental autoimmune encephalomyelitis represents the best characterized animal model as common clinical, histological, and immunological features are recapitulated. A label-free mass spectrometric proteomics approach was used to detect differences in protein abundance within specific fractions of disease-affected tissues including the soluble lysate derived from the spinal cord and membrane protein-enriched peripheral blood mononuclear cells. Tissues were harvested from actively induced experimental autoimmune encephalomyelitis mice and sham-induced ("vehicle" control) counterparts at the disease peak followed by subsequent analysis by nanoflow liquid chromatography tandem mass spectrometry. Relative protein quantitation was performed using both intensity- and fragmentation-based approaches. After statistical evaluation of the data, over 500 and 250 differentially abundant proteins were identified in the spinal cord and peripheral blood mononuclear cell data sets, respectively. More than half of these observations have not previously been linked to the disease. The biological significance of all candidate disease markers has been elucidated through rigorous literature searches, pathway analysis, and validation studies. Results from comprehensive targeted mass spectrometry analyses have confirmed the differential abundance of ∼ 200 candidate markers (≥ twofold dysregulated expression) at a 70% success rate. This study is, to our knowledge, the first to examine the cell-surface proteome of peripheral blood mononuclear cells in experimental autoimmune encephalomyelitis. These data provide a unique mechanistic insight into the dynamics of peripheral immune cell infiltration into CNS-privileged sites at a molecular level and has identified several candidate markers, which represent promising targets for future multiple sclerosis therapies. The mass spectrometry proteomics data associated with this manuscript have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000255.

The cellular redox environment alters antigen presentation.

Cysteine-containing peptides represent an important class of T cell epitopes, yet their prevalence remains underestimated. We have established and interrogated a database of around 70,000 naturally processed MHC-bound peptides and demonstrate that cysteine-containing peptides are presented on the surface of cells in an MHC allomorph-dependent manner and comprise on average 5-10% of the immunopeptidome. A significant proportion of these peptides are oxidatively modified, most commonly through covalent linkage with the antioxidant glutathione. Unlike some of the previously reported cysteine-based modifications, this represents a true physiological alteration of cysteine residues. Furthermore, our results suggest that alterations in the cellular redox state induced by viral infection are communicated to the immune system through the presentation of S-glutathionylated viral peptides, resulting in altered T cell recognition. Our data provide a structural basis for how the glutathione modification alters recognition by virus-specific T cells. Collectively, these results suggest that oxidative stress represents a mechanism for modulating the virus-specific T cell response.