Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome

Identification of TTLL8, POTEE, and PKMYT1 as immunogenic cancer-associated antigens and potential immunotherapy targets in ovarian cancer

Most high-grade serous ovarian cancers (OC) do not respond to current immunotherapies. To identify potential new actionable tumor antigens in OC, we performed immunopeptidomics on a human OC cell line expressing the HLA-A02:01 haplotype, which is commonly expressed across many racial and ethnic groups. From this dataset, we identified TTLL8, POTEE, and PKMYT1 peptides as candidate tumor antigens with low expression in normal tissues and upregulated expression in OC. Using tissue microarrays, we assessed the protein expression of TTLL8 and POTEE and their association with patient outcomes in a large cohort of OC patients. TTLL8 was found to be expressed in 56.7% of OC and was associated with a worse overall prognosis. POTEE was expressed in 97.2% of OC patients and had no significant association with survival. In patient TILs, increases in cytokine production and tetramer-positive populations identified antigen-specific CD8 T cell responses, which were dependent on antigen presentation by HLA class I. Antigen-specific T cells triggered cancer cell killing of antigen-pulsed OC cells. These findings suggest that TTLL8, POTEE, and PKMYT1 are potential targets for the development of antigen-targeted immunotherapy in OC.

In-silico development of multi-epitope subunit vaccine against lymphatic filariasis

The World Health Organization in 2022 reported that more than 863 million people in 50 countries are at risk of developing lymphatic filariasis (LF), a disease caused by parasitic infection. Immune responses to parasites suggest that the development of a prophylactic vaccine against LF is possible. Using a reverse vaccinology approach, the current study identified Trehalose-6-phosphatase (TPP) as a potential vaccine candidate among 15 reported vaccine antigens for B. malayi. High-ranking B and T-cell epitopes in the Trehalose-6-phosphatase (TPP) were shortlisted using online servers for subsequent analysis. We selected these peptides to construct a vaccine model using I-TASSER and GalaxyRefine server. The vaccine construct showed favorable physicochemical properties, high antigenicity, no allergenicity, no toxicity, and high stability. Structural validation using the Ramachandran plot showed that 98% of the residues were in favorable or mostly allowed regions. Molecular docking and simulation showed a strong binding affinity and stability of the subunit vaccine with toll-like receptor 4 (TLR4). Furthermore, the subunit vaccine showed a strong IgG/IgM response, with the disappearance of the antigen. We propose that our vaccine construct should be further evaluated using cellular and animal models to develop a vaccine that is safe and effective against LF.

Design of a peptide-based vaccine against human respiratory syncytial virus using a reverse vaccinology approach: evaluation of immunogenicity, antigenicity, allergenicity, and toxicity

Background

Attempts to develop an hRSV vaccine have faced safety and efficacy challenges, with only three FDA-approved vaccines (Moderna's Mresvia, Pfizer's Abrysvo, and GSK's Arexvy) available. These vaccines are limited to individuals over 60 years, require boosters, and only reduce disease severity without clearing the infection. Therefore, we employed a reverse vaccinology approach in this study to identify the most promising antigenic epitopes capable of eliciting a robust and protective immune response.

Methodology

This study employed computational techniques to design a novel multi-epitope vaccine targeting hRSV. Using bioinformatics tools, candidate epitopes were identified from conserved viral proteins (F and G glycoproteins), assessing their immunogenicity, antigenicity, and allergenicity. Key tools included ExPASy, ProtParam, VaxiJen v2.0, AllergenFP v1.0, AllerTOP v2.0, NetCTL v1.2, IEDB, and Toxin-Pred. The vaccine construct was assessed for stability and toxicity through in silico analyses. We then characterized its kinetic properties, evaluated its structural integrity, and analyzed its interactions with Toll-like receptors (TLRs) using molecular docking, modeling, and refinement with AlphaFold3 and ClusPro.

Results

The designed constructs showed strong antigenicity (0.5996 for F-based and 0.6048 for G-based vaccine), non-allergenicity, and stability (instability index <40). Among these, most amino acids were in the extracellular domain of the construct. Molecular docking and dynamics simulations indicated strong binding interactions with TLR1 and TLR4 and minimal RMSF fluctuations, which ensured structural stability. Strong humoral and cellular responses were suggested by in silico immune simulation demonstrating robust immune activation, with high levels of IgG, IgM, IL-2, and IFN-γ. The physical and chemical analyses revealed that the majority of amino acids from the F and G proteins were located in the extracellular domain of the construct. The presence of signal peptide cleavage sites in both glycoprotein components further facilitates antigen presentation to the immune system.

Conclusions

This study presents a promising peptide-based vaccine candidate against hRSV that can effectively engage the immune system, showing strong immunogenicity and antigenicity. Future in vitro and in vivo studies are essential to evaluate the ability of the multi-epitope vaccine candidate to stimulate both humoral and cell-mediated immune responses and to assess its efficacy and safety profile.

Diversifying T-cell responses: safeguarding against pandemic influenza with mosaic nucleoprotein

Pre-existing T-cell responses have been linked to reduced disease severity and better clinical outcomes during the 2009 influenza pandemic and the recent COVID-19 pandemic. We hypothesized that diversifying T-cell responses, particularly targeting conserved viral proteins such as the influenza A virus (IAV) nucleoprotein (NP), could protect against both epidemic and pandemic IAV strains. To test this, we created a mosaic nucleoprotein (MNP) by synthesizing a sequence that maximized the representation of 9-mer epitopes from 7422 NP sequences across human, swine, and avian IAVs. Notably, the MNP sequence showed high homology with the NP of the H5N1 strain affecting dairy cows in the ongoing outbreak. Mucosal immunization with the adjuvanted MNP vaccine induced robust CD8 and CD4 T-cell responses against both known immunodominant and in silico predicted subdominant epitopes. MNP-vaccinated mice challenged with epidemic H1N1 and H3N2 strains, which shared immunodominant CD8 and/or CD4 T-cell epitopes, showed a significant (~4 log) reduction in lung viral load. Importantly, MNP-vaccinated mice challenged with a pandemic H1N1 strain lacking shared immunodominant CD8 or CD4 epitopes exhibited a superior reduction in lung viral load, linked to T-cell responses targeting subdominant epitopes present in both the MNP and pandemic strain NP. These results suggest that a diversified T-cell response induced by the MNP vaccine could provide broad protection against severe disease from both current and emerging IAV strains.

IMPORTANCE

The World Health Organization (WHO) estimates that seasonal influenza causes 3-5 million cases of severe illness annually. The influenza virus frequently undergoes genetic changes through antigenic drift and antigenic shift, resulting in annual epidemics and occasional pandemics. Consequently, a major public health objective is to develop a universal influenza vaccine that offers broad protection against both current and pandemic influenza A strains. In this study, we designed a nucleoprotein (NP) antigen (termed mosaic NP) comprising antigenic regions found in thousands of influenza viruses, aiming to use it as a vaccine to induce broad anti-influenza T-cell responses. Our findings indicate that the mosaic NP vaccine provided significant protection against seasonal H1N1 and H3N2, as well as the pandemic H1N1 strain, demonstrating its effectiveness across various influenza subtypes. These findings suggest that the mosaic NP is a potential universal influenza vaccine antigen, capable of protecting against diverse strains of influenza viruses.

Immunogenic cryptic peptides dominate the antigenic landscape of ovarian cancer

Increased infiltration of CD3+ and CD8+ T cells into ovarian cancer (OC) is linked to better prognosis, but the specific antigens involved are unclear. Recent reports suggest that HLA class I can present peptides from noncoding genomic regions, known as noncanonical or cryptic peptides, but their immunogenicity is underexplored. To address this, we used immunopeptidomic analysis and RNA sequencing on five metastatic OC samples, which identified 311 cryptic peptides (40 to 83 per patient). Despite comprising less than 1% of total peptides, cryptic peptides from noncoding transcripts emerged as the predominant antigen class when compared to the other major classes of known tumor-specific and tumor-associated antigens in OC samples. Notably, nearly 70% of the prioritized cryptic peptides elicited T cell activation, as evidenced by increased 4-1BB and IFN-γ expression in autologous CD8+ T cells. This study reveals noncoding cryptic peptides as an important class of immunogenic antigens in OC.

Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets

T cells mediate pathogenesis of several autoimmune disorders by recognizing self-epitopes presented on Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) complex. The majority of autoantigens presented to T cells in various autoimmune disorders are not known, which has impeded autoantigen identification. Recent advances in immunopeptidomics have started to unravel the repertoire of antigenic epitopes presented on MHC. In several autoimmune diseases, immunopeptidomics has led to the identification of novel autoantigens and has enhanced our understanding of the mechanisms behind autoimmunity. Especially, immunopeptidomics has provided key evidence to explain the genetic risk posed by HLA alleles. In this review, we shed light on how immunopeptidomics can be leveraged to discover potential autoantigens. We highlight the application of immunopeptidomics in Type 1 Diabetes (T1D), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA). Finally, we highlight the practical considerations of implementing immunopeptidomics successfully and the technical challenges that need to be addressed. Overall, this review will provide an important context for using immunopeptidomics for understanding autoimmunity.

Genetic variation of hemolysin co-regulated protein 1 affects the immunogenicity and pathogenicity of Burkholderia pseudomallei

Hemolysin co-regulated protein 1 (Hcp1) is a component of the cluster 1 Type VI secretion system (T6SS1) that plays a key role during the intracellular lifecycle of Burkholderia pseudomallei. Hcp1 is recognized as a promising target antigen for developing melioidosis diagnostics and vaccines. While the gene encoding Hcp1 is retained across B. pseudomallei strains, variants of hcp1 have recently been identified. This study aimed to examine the prevalence of hcp1 variants in clinical isolates of B. pseudomallei, assess the antigenicity of the Hcp1 variants, and the ability of strains expressing these variants to stimulate multinucleated giant cell (MNGC) formation in comparison to strains expressing wild-type Hcp1 (Hcp1wt). Sequence analysis of 1,283 primary clinical isolates of B. pseudomallei demonstrated the presence of 8 hcp1 alleles encoding three types of Hcp1 proteins, including Hcp1wt (98.05%), Hcp1variant A (1.87%) and Hcp1variant B (0.08%). Compared to strains expressing Hcp1wt, those expressing the dominant variant, Hcp1variant A, stimulated lower levels of Hcp1variant A-specific antibody responses in melioidosis patients. Interestingly, when Hcp1variant A was expressed in B. pseudomallei K96243, this strain retained the ability to stimulate MNGC formation in A549 cells. In contrast, however, similar experiments with the Hcp1variant B demonstrated a decreased ability of B. pseudomallei to stimulate MNGC formation. Collectively, these results show that B. pseudomallei strains expressing variants of Hcp1 elicit variable antibody responses in melioidosis patients and differ in their ability to promote MNGC formation in cell culture.

Rational Design of an Epidermal Growth Factor Receptor Vaccine: Immunogenicity and Antitumor Research

The epidermal growth factor receptor (EGFR) is frequently overexpressed in a variety of human epithelial tumors, and its aberrant activation plays a pivotal role in promoting tumor growth, invasion, and metastasis. The clinically approved passive EGFR-related therapies have numerous limitations. Seven EGFR-ECD epitope peptides (EG1-7) were selected through bioinformatics epitope prediction tools including NetMHCpan-4.1, NetMHCIIpan-3.2, and IEDB Consensus (v2.18 and v2.22) and fused to the translocation domain of diphtheria toxin (DTT). The A549 tumor model was successfully established in a murine mouse model. The vaccine was formulated by combining the adjuvants Alum and CpG and subsequently assessed for its immunogenicity and anti-tumor efficacy. DTT-EG (3;5;6;7) vaccines elicited specific humoral and cellular immune responses and effectively suppressed tumor growth in both prophylactic and therapeutic mouse tumor models. The selected epitopes EG3 (HGAVRFSNNPALCNV145-159), EG5 (KDSLSINATNIKHFK346-360), EG6 (VKEITGFLLIQAWPE398-412), and EG7 (LCYANTINWKKLFGT469-483) were incorporated into vaccines for active immunization, representing a promising strategy for the treatment of tumors with overexpressed epidermal growth factor receptor (EGFR). The vaccine design and fusion method employed in this study demonstrate a viable approach toward the development of cancer vaccines.

Protective Antimicrobial Effect of the Potential Vaccine Created on the Basis of the Structure of the IgA1 Protease from Neisseria meningitidis

Background/Objectives: IgA1 protease is one of the virulence factors of Neisseria meningitidis, Haemophilus influenzae and other pathogens causing bacterial meningitis. The aim of this research is to create recombinant proteins based on fragments of the mature IgA1 protease A28-P1004 from N. meningitidis serogroup B strain H44/76. These proteins are potential components of an antimeningococcal vaccine for protection against infections caused by pathogenic strains of N. meningitidis and other bacteria producing serine-type IgA1 proteases. Methods: To obtain promising antigens for creating a vaccine, we designed and obtained several recombinant proteins. These proteins consisted of single or directly connected fragments selected from various regions of the IgA1 protease A28-P1004. The choice of these fragments was based on our calculated data on the distribution of linear and conformational B-cell epitopes and MHC-II T-cell epitopes in the structure of IgA1 protease, taking into account the physicochemical properties of potential compounds and the results of a comparative analysis of the spatial structures of the original IgA1 protease and potential recombinant proteins. We studied the immunogenic and protective effects of the obtained proteins on the BALB/c mice against meningococci of serogroups A, B and C. Results: Proteins MA28-P1004-LEH6, MW140-K833-LEH6, MW329-P1004-LEH6, M(W140-H328)-(W412-D604)-(Y866-P1004)-LEH6 and M(W140-Q299)-(Y866-P1004)-LEH6 have shown the following antibody titers, 103/titer: 11 ± 1, 6 ± 2, 6 ± 1, 9 ± 1 and 22 ± 3, respectively. Also, the last two proteins have shown the best average degree of protection from N. meningitidis serogroups A, B and C, %: 62 ± 6, 63 ± 5, 67 ± 4 respectively for M(W140-H328)-(W412-D604)-(Y866-P1004)-LEH6 and 70 ± 5, 66 ± 6, 83 ± 3 respectively for M(W140-Q299)-(Y866-P1004)-LEH6. Conclusions: We selected two recombinant proteins consisting of two (M(W140-Q299)-(Y866-P1004)-LEH6) or three (M(W140-H328)-(W412-D604)-(Y866-P1004)-LEH6) linked fragments of IgA1 protease A28-P1004 as candidate active component for an antimeningococcal vaccine.

Selective HLA Class II Allele-Restricted Activation of Atabecestat Metabolite-Specific Human T-Cells

Elevations in hepatic enzymes were detected in several trial patients exposed to the Alzheimer's drug atabecestat, which resulted in termination of the drug development program. Characterization of hepatic T-lymphocyte infiltrates and diaminothiazine (DIAT) metabolite-responsive, human leukocyte antigen (HLA)-DR-restricted, CD4+ T-lymphocytes in the blood of patients confirmed an immune pathogenesis. Patients with immune-mediated liver injury expressed a restricted panel of HLA-DRB1 alleles including HLA-DRB1*12:01, HLA-DRB1*13:02, and HLA-DRB1*15:01. Thus, the objectives of this study were to (i) generate DIAT-responsive T-cell clones from HLA-genotyped drug-naive donors, (ii) characterize pathways of DIAT-specific T-cell activation, and (iii) assess HLA allele restriction of the DIAT-specific T-cell response. Sixteen drug-naive donors expressing the HLA-DR molecules outlined above were recruited, and T-cell clones were generated. Cellular phenotype, function, and HLA-allele restriction were assessed using culture assays. Peptides displayed by HLA class II molecules in the presence and absence of atabecestat were analyzed by mass spectrometry. Several DIAT-responsive CD4+ clones, displaying no reactivity toward the parent drug, were successfully generated from donors expressing HLA-DRB1*12:01, HLA-DRB1*13:02, and HLA-DRB1*15:01 but not from other donors expressing other HLA-DRB1 alleles. T-cell clones were activated following direct binding of DIAT to HLA-DR proteins expressed on the surface of antigen presenting cells. DIAT binding did not alter the HLA-DRB1 peptide binding repertoire, indicative of a binding interaction with the HLA-associated peptide rather than with the HLA protein itself. DIAT-specific T-cell responses displayed HLA-DRB1*12:01, HLA-DRB1*13:02, and HLA-DRB1*15:01 restriction. These data demonstrate that DIAT displays a degree of selectivity toward HLA protein and associated peptides, with expression of certain alleles increasing and that of others decreasing, the likelihood that a drug-specific T-cell response develops.

Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain

Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.

Gene and protein sequence features augment HLA class I ligand predictions

The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.

Identifying major histocompatibility complex class II-DR molecules in bovine and swine peripheral blood monocyte-derived macrophages using mAb-L243

Major histocompatibility complex class II (MHC-II) molecules are involved in immune responses against pathogens and vaccine candidates' immunogenicity. Immunopeptidomics for identifying cancer and infection-related antigens and epitopes have benefited from advances in immunopurification methods and mass spectrometry analysis. The mouse anti-MHC-II-DR monoclonal antibody L243 (mAb-L243) has been effective in recognising MHC-II-DR in both human and non-human primates. It has also been shown to cross-react with other animal species, although it has not been tested in livestock. This study used mAb-L243 to identify Staphylococcus aureus and Salmonella enterica serovar Typhimurium peptides binding to cattle and swine macrophage MHC-II-DR molecules using flow cytometry, mass spectrometry and two immunopurification techniques. Antibody cross-reactivity led to identifying expressed MHC-II-DR molecules, together with 10 Staphylococcus aureus peptides in cattle and 13 S. enterica serovar Typhimurium peptides in swine. Such data demonstrates that MHC-II-DR expression and immunocapture approaches using L243 mAb represents a viable strategy for flow cytometry and immunopeptidomics analysis of bovine and swine antigen-presenting cells.

New light on the HLA-DR immunopeptidomic landscape

The set of peptides processed and presented by major histocompatibility complex class II molecules defines the immunopeptidome, and its characterization holds keys to understanding essential properties of the immune system. High-throughput mass spectrometry (MS) techniques enable interrogation of the diversity and complexity of the immunopeptidome at an unprecedented scale. Here, we analyzed a large set of MS immunopeptidomics data from 40 donors, 221 samples, covering 30 unique HLA-DR molecules. We identified likely co-immunoprecipitated HLA-DR irrelevant contaminants using state-of-the-art prediction methods and unveiled novel light on the properties of HLA antigen processing and presentation. The ligandome (HLA binders) was enriched in 15-mer peptides, and the contaminome (nonbinders) in longer peptides. Classification of singletons and nested sets showed that the first were enriched in contaminants. Investigating the source protein location of ligands revealed that only contaminants shared a positional bias. Regarding subcellular localization, nested peptides were found to be predominantly of endolysosomal origin, whereas singletons shared an equal distribution between the cytosolic and endolysosomal origin. According to antigen-processing signatures, no significant differences were observed between the cytosolic and endolysosomal ligands. Further, the sensitivity of MS immunopeptidomics was investigated by analyzing overlap and saturation between biological MS replicas, concluding that at least 5 replicas are needed to identify 80% of the immunopeptidome. Moreover, the overlap in immunopeptidome between donors was found to be very low both in terms of peptides and source proteins, the latter indicating a critical HLA bias in the antigen sampling in the HLA antigen presentation. Finally, the complementarity between MS and in silico approaches for comprehensively sampling the immunopeptidome was demonstrated.

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

PBITV3 : A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates

Rise of life-threatening superbugs, pandemics and epidemics warrants the need for cost-effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high-throughput discovery platforms to prioritize potential drug-targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug-targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad-spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno-informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug-resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/.

Divergent HLA variations and heterogeneous expression but recurrent HLA loss-of- heterozygosity and common HLA-B and TAP transcriptional silencing across advanced pediatric solid cancers

The human leukocyte antigen (HLA) system is a major factor controlling cancer immunosurveillance and response to immunotherapy, yet its status in pediatric cancers remains fragmentary. We determined high-confidence HLA genotypes in 576 children, adolescents and young adults with recurrent/refractory solid tumors from the MOSCATO-01 and MAPPYACTS trials, using normal and tumor whole exome and RNA sequencing data and benchmarked algorithms. There was no evidence for narrowed HLA allelic diversity but discordant homozygosity and allele frequencies across tumor types and subtypes, such as in embryonal and alveolar rhabdomyosarcoma, neuroblastoma MYCN and 11q subtypes, and high-grade glioma, and several alleles may represent protective or susceptibility factors to specific pediatric solid cancers. There was a paucity of somatic mutations in HLA and antigen processing and presentation (APP) genes in most tumors, except in cases with mismatch repair deficiency or genetic instability. The prevalence of loss-of-heterozygosity (LOH) ranged from 5.9 to 7.7% in HLA class I and 8.0 to 16.7% in HLA class II genes, but was widely increased in osteosarcoma and glioblastoma (~15-25%), and for DRB1-DQA1-DQB1 in Ewing sarcoma (~23-28%) and low-grade glioma (~33-50%). HLA class I and HLA-DR antigen expression was assessed in 194 tumors and 44 patient-derived xenografts (PDXs) by immunochemistry, and class I and APP transcript levels quantified in PDXs by RT-qPCR. We confirmed that HLA class I antigen expression is heterogeneous in advanced pediatric solid tumors, with class I loss commonly associated with the transcriptional downregulation of HLA-B and transporter associated with antigen processing (TAP) genes, whereas class II antigen expression is scarce on tumor cells and occurs on immune infiltrating cells. Patients with tumors expressing sufficient HLA class I and TAP levels such as some glioma, osteosarcoma, Ewing sarcoma and non-rhabdomyosarcoma soft-tissue sarcoma cases may more likely benefit from T cell-based approaches, whereas strategies to upregulate HLA expression, to expand the immunopeptidome, and to target TAP-independent epitopes or possibly LOH might provide novel therapeutic opportunities in others. The consequences of HLA class II expression by immune cells remain to be established. Immunogenetic profiling should be implemented in routine to inform immunotherapy trials for precision medicine of pediatric cancers.

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.

In Silico Tools for Predicting Novel Epitopes

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

In Silico Design of a New Epitope-Based Vaccine against Grass Group 1 Allergens

Allergic diseases are a global public health problem that affects up to 30% of the population in industrialized societies. More than 40% of allergic patients suffer from grass pollen allergy. Grass pollen allergens of group 1 and group 5 are the major allergens, since they induce allergic reactions in patients at high rates. In this study, we used immunoinformatic approaches to design an effective epitope-based vaccine against the grass group 1 allergens. After the alignment of all known pollen T-cell and B-cell epitopes from pollen allergens available in the public databases, the epitope GTKSEVEDVIPEGWKADTSY was identified as the most suitable for further analyses. The target sequence was subjected to immunoinformatics analyses to predict antigenic T-cell and B-cell epitopes. Population coverage analysis was performed for CD8+ and CD4+ T-cell epitopes. The selected T-cell epitopes (VEDVIPEGW and TKSEVEDVIPEGWKA) covered 78.87% and 98.20% of the global population and 84.57% and 99.86% of the population of Europe. Selected CD8+, CD4+ T-cell and B-cell epitopes have been validated by molecular docking analysis. CD8+ and CD4+ T-cell epitopes showed a very strong binding affinity to major histocompatibility complex (MHC) class I (MHC I) molecules and MHC class II (MHC II) molecules with global energy scores of -72.1 kcal/mol and -89.59 kcal/mol, respectively. The human IgE-Fc (PDB ID 4J4P) showed a lower affinity with B-cell epitope (ΔG = -34.4 kcal/mol), while the Phl p 2-specific human IgE Fab (PDB ID 2VXQ) had the lowest binding with the B-cell epitope (ΔG = -29.9 kcal/mol). Our immunoinformatics results demonstrated that the peptide GTKSEVEDVIPEGWKADTSY could stimulate the immune system and we performed ex vivo tests showed that the investigated epitope activates T cells isolated from patients with grass pollen allergy, but it is not recognized by IgE antibodies specific for grass pollen allergens. This confirms the importance of such studies to establish universal epitopes to serve as a basis for developing an effective vaccine against a particular group of allergens. Further in vivo studies are needed to validate the effectiveness of such a vaccine against grass pollen allergens.

Expanding the MAPPs Assay to Accommodate MHC-II Pan Receptors for Improved Predictability of Potential T Cell Epitopes

A critical step in the immunogenicity cascade is attributed to human leukocyte antigen (HLA) II presentation triggering T cell immune responses. The liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based major histocompatibility complex (MHC) II-associated peptide proteomics (MAPPs) assay is implemented during preclinical risk assessments to identify biotherapeutic-derived T cell epitopes. Although studies indicate that HLA-DP and HLA-DQ alleles are linked to immunogenicity, most MAPPs studies are restricted to using HLA-DR as the dominant HLA II genotype due to the lack of well-characterized immunoprecipitating antibodies. Here, we address this issue by testing various commercially available clones of MHC-II pan (CR3/43, WR18, and Tü39), HLA-DP (B7/21), and HLA-DQ (SPV-L3 and 1a3) antibodies in the MAPPs assay, and characterizing identified peptides according to binding specificity. Our results reveal that HLA II receptor-precipitating reagents with similar reported specificities differ based on clonality and that MHC-II pan antibodies do not entirely exhibit pan-specific tendencies. Since no individual antibody clone is able to recover the complete HLA II peptide repertoire, we recommend a mixed strategy of clones L243, WR18, and SPV-L3 in a single immunoprecipitation step for more robust compound-specific peptide detection. Ultimately, our optimized MAPPs strategy improves the predictability and additional identification of T cell epitopes in immunogenicity risk assessments.

The HLA class I immunopeptidomes of AAV capsid proteins

Introduction

Cellular immune responses against AAV vector capsid represent an obstacle for successful gene therapy. Previous studies have used overlapping peptides spanning the entire capsid sequence to identify T cell epitopes recognized by AAV-specific CD8+ T cells. However, the repertoire of peptides naturally displayed by HLA class I molecules for CD8 T cell recognition is unknown.

Methods

Using mRNA transfected monocyte-derived dendritic cells (MDDCs) and MHC-associated peptide proteomics (MAPPs), we identified the HLA class I immunopeptidomes of AAV2, AAV6 and AAV9 capsids. MDDCs were isolated from a panel of healthy donors that have diverse alleles across the US population. mRNA-transfected MDDCs were lysed, the peptide:HLA complexes immunoprecipitated, and peptides eluted and analyzed by mass spectrometry.

Results

We identified 65 AAV capsid-derived peptides loaded on HLA class I molecules of mRNA transfected monocyte derived dendritic cells. The HLA class I peptides are distributed along the entire capsid and more than 60% are contained within HLA class II clusters. Most of the peptides are organized as single species, however we identified twelve clusters containing at least 2 peptides of different lengths. Only 9% of the identified peptides have been previously identified as T cell epitopes, demonstrating that the immunogenicity potential for the vast majority of the AAV HLA class I immunopeptidome remains uncharacterized. In contrast, 12 immunogenic epitopes identified before were not found to be naturally processed in our study. Remarkably, 11 naturally presented AAV peptides were highly conserved among the three serotypes analyzed suggesting the possibility of cross-reactive AAV-specific CD8 T cells.

Discussion

This work is the first comprehensive study identifying the naturally displayed HLA class I peptides derived from the capsid of AAVs. The results from this study can be used to generate strategies to assess immunogenicity risk and cross-reactivity among serotypes during gene therapies.

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

SARS-CoV-2 Gut-Targeted Epitopes: Sequence Similarity and Cross-Reactivity Join Together for Molecular Mimicry

The gastrointestinal tract can be heavily infected by SARS-CoV-2. Being an auto-immunogenic virus, SARS-CoV-2 represents an environmental factor that might play a role in gut-associated autoimmune diseases. However, molecular mimicry between the virus and the intestinal epitopes is under-investigated. The present study aims to elucidate sequence similarity between viral antigens and human enteric sequences, based on known cross-reactivity. SARS-CoV-2 epitopes that cross-react with human gut antigens were explored, and sequence alignment was performed against self-antigens implicated in enteric autoimmune conditions. Experimental SARS-CoV-2 epitopes were aggregated from the Immune Epitope Database (IEDB), while enteric antigens were obtained from the UniProt Knowledgebase. A Pairwise Local Alignment tool, EMBOSS Matcher, was employed for the similarity search. Sequence similarity and targeted cross-reactivity were depicted between 10 pairs of immunoreactive epitopes. Similar pairs were found in four viral proteins and seven enteric antigens related to ulcerative colitis, primary biliary cholangitis, celiac disease, and autoimmune hepatitis. Antibodies made against the viral proteins that were cross-reactive with human gut antigens are involved in several essential cellular functions. The relationship and contribution of those intestinal cross-reactive epitopes to SARS-CoV-2 or its potential contribution to gut auto-immuno-genesis are discussed.

Unraveling the glycosylated immunopeptidome with HLA-Glyco

Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome

Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and β-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific mass spectrometry immunopeptidomics data. The analysis demonstrates highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants reveals a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study furthers our understanding of HLA-DQ specificities and casts light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2 .

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

CD4⁺ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4⁺ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4⁺ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

The MHC Motif Atlas: a database of MHC binding specificities and ligands

The highly polymorphic Major Histocompatibility Complex (MHC) genes are responsible for the binding and cell surface presentation of pathogen or cancer specific T-cell epitopes. This process is fundamental for eliciting T-cell recognition of infected or malignant cells. Epitopes displayed on MHC molecules further provide therapeutic targets for personalized cancer vaccines or adoptive T-cell therapy. To help visualizing, analyzing and comparing the different binding specificities of MHC molecules, we developed the MHC Motif Atlas (http://mhcmotifatlas.org/). This database contains information about thousands of class I and class II MHC molecules, including binding motifs, peptide length distributions, motifs of phosphorylated ligands, multiple specificities or links to X-ray crystallography structures. The database further enables users to download curated datasets of MHC ligands. By combining intuitive visualization of the main binding properties of MHC molecules together with access to more than a million ligands, the MHC Motif Atlas provides a central resource to analyze and interpret the binding specificities of MHC molecules.

The HLA class-II immunopeptidomes of AAV capsids proteins

Introduction

Gene therapies are using Adeno-associated viruses (AAVs) as vectors, but immune responses against the capsids pose challenges to their efficiency and safety. Helper T cell recognition of capsid-derived peptides bound to human leukocyte antigen (HLA) class II molecules is an essential step in the AAV-specific adaptive immunity.

Methods

Using MHC-associated peptide proteomics, we identified the HLA-DR and HLA-DQ immunopeptidomes of the capsid proteins of three different AAV serotypes (AAV2, AAV6, and AAV9) from a panel of healthy donors selected to represent a majority of allele usage.

Results

The identified sequences span the capsids of all serotypes, with AAV2 having the highest peptide count. For all the serotypes, multiple promiscuous peptides were identified and displayed by both HLA-DR and -DQ. However, despite high sequence homology, there were few identical peptides among AAV2, AAV6, and AAV9 immunopeptidomes, and none were promiscuous.

Discussion

Results from this work represent a comprehensive immunopeptidomics research of potential CD4+ T cell epitopes and provide the basis for immunosurveillance efforts for safer and more efficient AAV-based gene therapies.

Subcellular location of source proteins improves prediction of neoantigens for immunotherapy

Antigen presentation via the major histocompatibility complex (MHC) is essential for anti-tumor immunity. However, the rules that determine which tumor-derived peptides will be immunogenic are still incompletely understood. Here, we investigated whether constraints on peptide accessibility to the MHC due to protein subcellular location are associated with peptide immunogenicity potential. Analyzing over 380,000 peptides from studies of MHC presentation and peptide immunogenicity, we find clear spatial biases in both eluted and immunogenic peptides. We find that including parent protein location improves the prediction of peptide immunogenicity in multiple datasets. In human immunotherapy cohorts, the location was associated with a neoantigen vaccination response, and immune checkpoint blockade responders generally had a higher burden of neopeptides from accessible locations. We conclude that protein subcellular location adds important information for optimizing cancer immunotherapies.

SARS-CoV-2: Immunopeptidomics and Other Immunological Studies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced a significant continuing epidemic worldwide [...]

Localized ablative immunotherapy drives de novo CD8+ T-cell responses to poorly immunogenic tumors

BACKGROUND

Localized ablative immunotherapies hold great promise in stimulating antitumor immunity to treat metastatic and poorly immunogenic tumors. Tumor ablation is well known to release tumor antigens and danger-associated molecular patterns to stimulate T-cell immunity, but its immune stimulating effect is limited, particularly against metastatic tumors.

METHODS

In this study, we combined photothermal therapy with a potent immune stimulant, N-dihydrogalactochitosan, to create a local ablative immunotherapy which we refer to as laser immunotherapy (LIT). Mice bearing B16-F10 tumors were treated with LIT when the tumors reached 0.5 cm3 and were monitored for survival, T-cell activation, and the ability to resist tumor rechallenge.

RESULTS

We found that LIT stimulated a stronger and more consistent antitumor T-cell response to the immunologically 'cold' B16-F10 melanoma tumors and conferred a long-term antitumor memory on tumor rechallenge. Furthermore, we discovered that LIT generated de novo CD8+ T-cell responses that strongly correlated with animal survival and tumor rejection.

CONCLUSION

In summary, our findings demonstrate that LIT enhances the activation of T cells and drives de novo antitumor T-cell responses. The data presented herein suggests that localized ablative immunotherapies have great potential to synergize with immune checkpoint therapies to enhance its efficacy, resulting in improved antitumor immunity.

The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome

INTRODUCTION

The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data.

AREAS COVERED

Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches.

EXPERT OPINION

Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.