Characterisation of novel influenza-derived HLA-B*18:01-restricted epitopes.

Objectives: Seasonal influenza viruses cause roughly 650 000 deaths annually despite available vaccines. CD8+ T cells typically recognise influenza-derived peptides from internal structural and non-structural influenza proteins and are an attractive avenue for future vaccine design as they could reduce the severity of disease following infection with diverse influenza strains. CD8+ T cells recognise peptides presented by the highly polymorphic Human Leukocyte Antigens class I molecules (HLA-I). Each HLA-I variant has distinct peptide binding preferences, representing a significant obstacle for designing vaccines that elicit CD8+ T cell responses across broad populations. Consequently, the rational design of a CD8+ T cell-mediated vaccine would require the identification of highly immunogenic peptides restricted to a range of different HLA molecules. Methods: Here, we assessed the immunogenicity of six recently published novel influenza-derived peptides identified by mass-spectrometry and predicted to bind to the prevalent HLA-B*18:01 molecule. Results: Using CD8+ T cell activation assays and protein biochemistry, we showed that 3/6 of the novel peptides were immunogenic in several HLA-B*18:01+ individuals and confirmed their HLA-B*18:01 restriction. We subsequently compared CD8+ T cell responses towards the previously identified highly immunogenic HLA-B*18:01-restricted NP219 peptide. Using X-ray crystallography, we solved the first crystal structures of HLA-B*18:01 presenting immunogenic influenza-derived peptides. Finally, we dissected the first TCR repertoires specific for HLA-B*18:01 restricted pathogen-derived peptides, identifying private and restricted repertoires against each of the four peptides. Conclusion: Overall the characterisation of these novel immunogenic peptides provides additional HLA-B*18:01-restricted vaccine targets derived from the Matrix protein 1 and potentially the non-structural protein and the RNA polymerase catalytic subunit of influenza viruses.

Antigen-specific immunotherapy via delivery of tolerogenic dendritic cells for multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system resulting from loss of immune tolerance. Many disease-modifying therapies for MS have broad immunosuppressive effects on peripheral immune cells, but this can increase risks of infection and attenuate vaccine-elicited immunity. A more targeted approach is to re-establish immune tolerance in an autoantigen-specific manner. This review discusses methods to achieve this, focusing on tolerogenic dendritic cells. Clinical trials in other autoimmune diseases also provide learnings with regards to clinical translation of this approach, including identification of autoantigen(s), selection of appropriate patients and administration route and frequency.

CD8+ T-cell responses towards conserved influenza B virus epitopes across anatomical sites and age.

Influenza B viruses (IBVs) cause substantive morbidity and mortality, and yet immunity towards IBVs remains understudied. CD8+ T-cells provide broadly cross-reactive immunity and alleviate disease severity by recognizing conserved epitopes. Despite the IBV burden, only 18 IBV-specific T-cell epitopes restricted by 5 HLAs have been identified currently. A broader array of conserved IBV T-cell epitopes is needed to develop effective cross-reactive T-cell based IBV vaccines. Here we identify 9 highly conserved IBV CD8+ T-cell epitopes restricted to HLA-B*07:02, HLA-B*08:01 and HLA-B*35:01. Memory IBV-specific tetramer+CD8+ T-cells are present within blood and tissues. Frequencies of IBV-specific CD8+ T-cells decline with age, but maintain a central memory phenotype. HLA-B*07:02 and HLA-B*08:01-restricted NP30-38 epitope-specific T-cells have distinct T-cell receptor repertoires. We provide structural basis for the IBV HLA-B*07:02-restricted NS1196-206 (11-mer) and HLA-B*07:02-restricted NP30-38 epitope presentation. Our study increases the number of IBV CD8+ T-cell epitopes, and defines IBV-specific CD8+ T-cells at cellular and molecular levels, across tissues and age.

A Simple and Rapid Protocol for the Isolation of Murine Bone Marrow Suitable for the Differentiation of Dendritic Cells.

The generation of bone-marrow-derived dendritic cells is a widely used approach in immunological research to study antigen processing and presentation, as well as T-cell activation responses. However, the initial step of isolating the bone marrow can be time-consuming, especially when larger numbers of precursor cells are required. Here, we assessed whether an accelerated bone marrow isolation method using centrifugation is suitable for the differentiation of FMS-like tyrosine kinase 3 ligand-driven dendritic cells. Compared to the conventional flushing method, the centrifugation-based isolation method resulted in a similar bone marrow cell yield on Day 0, increased cell numbers by Day 8, similar proportions of dendritic cell subsets, and consequently a higher number of type 1 conventional dendritic cells (cDC1) from the culture. Although the primary purpose of this method of optimization was to improve experimental efficiency and increase the output of cDC1s, the protocol is also compatible with the differentiation of other dendritic cell subsets such as cDC2 and plasmacytoid dendritic cells, with an improved output cell count and a consistent phenotype.

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.

DNAJB1-PRKACA fusion neoantigens elicit rare endogenous T cell responses that potentiate cell therapy for fibrolamellar carcinoma.

Fibrolamellar carcinoma (FLC) is a liver tumor with a high mortality burden and few treatment options. A promising therapeutic vulnerability in FLC is its driver mutation, a conserved DNAJB1-PRKACA gene fusion that could be an ideal target neoantigen for immunotherapy. In this study, we aim to define endogenous CD8 T cell responses to this fusion in FLC patients and evaluate fusion-specific T cell receptors (TCRs) for use in cellular immunotherapies. We observe that fusion-specific CD8 T cells are rare and that FLC patient TCR repertoires lack large clusters of related TCR sequences characteristic of potent antigen-specific responses, potentially explaining why endogenous immune responses are insufficient to clear FLC tumors. Nevertheless, we define two functional fusion-specific TCRs, one of which has strong anti-tumor activity in vivo. Together, our results provide insights into the fragmented nature of neoantigen-specific repertoires in humans and indicate routes for clinical development of successful immunotherapies for FLC.

Profound N-glycan remodelling accompanies MHC-II immunopeptide presentation.

Immunopeptidomics, the study of peptide antigens presented on the cell surface by the major histocompatibility complex (MHC), offers insights into how our immune system recognises self/non-self in health and disease. We recently discovered that hyper-processed (remodelled) N-glycans are dominant features decorating viral spike immunopeptides presented via MHC-class II (MHC-II) molecules by dendritic cells pulsed with SARS-CoV-2 spike protein, but it remains unknown if endogenous immunopeptides also undergo N-glycan remodelling. Taking a multi-omics approach, we here interrogate published MHC-II immunopeptidomics datasets of cultured monocyte-like (THP-1) and breast cancer-derived (MDA-MB-231) cell lines for overlooked N-glycosylated peptide antigens, which we compare to their source proteins in the cellular glycoproteome using proteomics and N-glycomics data from matching cell lines. Hyper-processed chitobiose core and paucimannosidic N-glycans alongside under-processed oligomannosidic N-glycans were found to prevalently modify MHC-II-bound immunopeptides isolated from both THP-1 and MDA-MB-231, while complex/hybrid-type N-glycans were (near-)absent in the immunopeptidome as supported further by new N-glycomics data generated from isolated MHC-II-bound peptides derived from MDA-MB-231 cells. Contrastingly, the cellular proteomics and N-glycomics data from both cell lines revealed conventional N-glycosylation rich in complex/hybrid-type N-glycans, which, together with the identification of key lysosomal glycosidases, suggest that MHC-II peptide antigen processing is accompanied by extensive N-glycan trimming. N-glycan remodelling appeared particularly dramatic for cell surface-located glycoproteins while less remodelling was observed for lysosomal-resident glycoproteins. Collectively, our findings indicate that both under- and hyper-processed N-glycans are prevalent features of endogenous MHC-II immunopeptides, an observation that demands further investigation to enable a better molecular-level understanding of immune surveillance.

A tactile approach to introduce the skin autoimmune disease psoriasis to the general public and the vision-impaired community.

Scientific outreach activities play an important role in disseminating knowledge, connecting the general public to research and breaking down scientific skepticism barriers. However, the vision-impaired community is often disadvantaged when the most common audio-visual approach of scientific communication is applied. Here we integrated tactile clues in the scientific communication of immune processes involved in the autoimmune skin disease psoriasis. We fostered the involvement of the vision-impaired community through interactive experiences, including tactile scientific origami art, a haptic poster and wood-carved molecular models. Readily accessible science communication that engages a number of senses is a critical step toward making science more inclusive and engaging for individuals with a wide range of sensory abilities. The approach of the 2023 Monash Sensory Science exhibition aligns with the principles of equity, diversity and inclusion and helps to empower a more informed and scientifically literate public.

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.

Need to know which MHCs protect against COVID? There's an App for that!

In this article, we discuss the recent observation by Augusto et al. made using a novel mobile phone application-based COVID-19 Citizen Science Study that an HLA genetic variant, HLA-B*15:01, is associated with asymptomatic SARS-CoV-2 infection. To explain this association, Augusto et al. describe a cross-reactive memory CD8+ T-cell response in HLA-B*15:01+ SARS-CoV-2 unexposed individuals that retains high avidity for two structurally conserved epitopes found in SARS-CoV-2 and seasonal coronavirus strains. These observations provide an insight into potential molecular determinants that facilitate rapid, early clearance of virus.

The allopurinol metabolite, oxypurinol, drives oligoclonal expansions of drug-reactive T cells in resolved hypersensitivity cases and drug-naïve healthy donors.

Allopurinol (ALP) is a successful drug used in the treatment of gout. However, this drug has been implicated in hypersensitivity reactions that can cause severe to life-threatening reactions such as Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). Individuals who carry the human leukocyte antigen (HLA)-B*58:01 allotype are at higher risk of experiencing a hypersensitivity reaction (odds ratios ranging from 5.62 to 580.3 for mild to severe reactions, respectively). In addition to the parent drug, the metabolite oxypurinol (OXP) is implicated in triggering T cell-mediated immunopathology via a labile interaction with HLA-B*58:01. To date, there has been limited information regarding the T-cell receptor (TCR) repertoire usage of reactive T cells in patients with ALP-induced SJS or TEN and, in particular, there are no reports examining paired αßTCRs. Here, using in vitro drug-treated PBMCs isolated from both resolved ALP-induced SJS/TEN cases and drug-naïve healthy donors, we show that OXP is the driver of CD8+ T cell-mediated responses and that drug-exposed memory T cells can exhibit a proinflammatory immunophenotype similar to T cells described during active disease. Furthermore, this response supported the pharmacological interaction with immune receptors (p-i) concept by showcasing (i) the labile metabolite interaction with peptide/HLA complexes, (ii) immunogenic complex formation at the cell surface, and (iii) lack of requirement for antigen processing to elicit drug-induced T cell responsiveness. Examination of paired OXP-induced αßTCR repertoires highlighted an oligoclonal and private clonotypic profile in both resolved ALP-induced SJS/TEN cases and drug-naïve healthy donors.

Complimentary electrostatics dominate T-cell receptor binding to a psoriasis-associated peptide antigen presented by human leukocyte antigen C∗06:02.

Psoriasis is a chronic skin disease characterized by hyperproliferative epidermal lesions infiltrated by autoreactive T cells. Individuals expressing the human leukocyte antigen (HLA) C∗06:02 allele are at highest risk for developing psoriasis. An autoreactive T cell clone (termed Vα3S1/Vß13S1) isolated from psoriatic plaques is selective for HLA-C∗06:02, presenting a peptide derived from the melanocyte-specific autoantigen ADAMTSL5 (VRSRRCLRL). Here we determine the crystal structure of this psoriatic TCR-HLA-C∗06:02 ADAMTSL5 complex with a stabilized peptide. Docking of the TCR involves an extensive complementary charge network formed between negatively charged TCR residues interleaving with exposed arginine residues from the self-peptide and the HLA-C∗06:02 α1 helix. We probed these interactions through mutagenesis and activation assays. The charged interface spans the polymorphic region of the C1/C2 HLA group. Notably the peptide-binding groove of HLA-C∗06:02 appears exquisitely suited for presenting highly charged Arg-rich epitopes recognized by this acidic psoriatic TCR. Overall, we provide a structural basis for understanding the engagement of melanocyte antigen-presenting cells by a TCR implicated in psoriasis while simultaneously expanding our knowledge of how TCRs engage HLA-C.

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.

A population pharmacokinetic model of polymyxin B based on prospective clinical data to inform dosing in hospitalized patients.

OBJECTIVES: To develop a population pharmacokinetic (PK) model with data from the largest polymyxin B-treated patient population studied to date to optimize its dosing in hospitalized patients. METHODS: Hospitalized patients receiving intravenous polymyxin B for ≥48 hours were enrolled. Blood samples were collected at steady state and drug concentrations were analysed by liquid chromotography tandem mass spectrometry (LC-MS/MS). Population PK analysis and Monte Carlo simulations were performed to determine the probability of target attainment (PTA). RESULTS: One hundred and forty-two patients received intravenous polymyxin B (1.33-6 mg/kg/day), providing 681 plasma samples. Twenty-four patients were on renal replacement therapy, including 13 on continuous veno-venous hemodiafiltration (CVVHDF). A 2-compartment model adequately described the PK with body weight as a covariate on the volume of distribution that affected Cmax, but it did not impact clearance or exposure. Creatinine clearance was a statistically significant covariate on clearance, although clinically relevant variations of dose-normalized drug exposure were not observed across a wide creatinine clearance range. The model described higher clearance in CVVHDF patients than in non-CVVHDF patients. Maintenance doses of ≥2.5 mg/kg/day or ≥150 mg/day had a PTA ≥90% (for non-pulmonary infections target) at a steady state for minimum inhibitory concentrations ≤2 mg/L. The PTA at a steady state for CVVHDF patients was lower. DISCUSSION: Fixed loading and maintenance doses of polymyxin B seemed to be more appropriate than weight-based dosing regimens in patients weighing 45-90 kg. Higher doses may be needed in patients on CVVHDF. Substantial variability in polymyxin B clearance and volume of distribution was found, suggesting that therapeutic drug monitoring may be indicated.

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.

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. RESULTS: Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. AVAILABILITY AND IMPLEMENTATION: PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

TCR_Explore: A novel webtool for T cell receptor repertoire analysis.

T cells expressing either alpha-beta or gamma-delta T cell receptors (TCR) are critical sentinels of the adaptive immune system, with receptor diversity being essential for protective immunity against a broad array of pathogens and agents. Programs available to profile TCR clonotypic signatures can be limiting for users with no coding expertise. Current analytical pipelines can be inefficient due to manual processing steps, open to data entry errors and have multiple analytical tools with unique inputs that require coding expertise. Here we present a bespoke webtool designed for users irrespective of coding expertise, coined 'TCR_Explore', enabling analysis either derived via Sanger sequencing or next generation sequencing (NGS) platforms. Further, TCR_Explore incorporates automated quality control steps for Sanger sequencing. The creation of flexible and publication ready figures are enabled for different sequencing platforms following universal conversion to the TCR_Explore file format. TCR_Explore will enhance a user's capacity to undertake in-depth TCR repertoire analysis of both new and pre-existing datasets for identification of T cell clonotypes associated with health and disease. The web application is located at https://tcr-explore.erc.monash.edu for users to interactively explore TCR repertoire datasets.