Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction.

Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

α-Mannosylated HLA-II glycopeptide antigens dominate the immunopeptidome of immortalised cells and tumour tissues.

Immunopeptides are cell surface-located protein fragments that aid our immune system to recognise and respond to pathogenic insult and malignant transformation. In this two-part communication, we firstly summarise and reflect on our recent discovery documenting that MHC-II-bound immunopeptides from immortalised cell lines prevalently carry N-glycans that differ from the cellular glycoproteome (Goodson, Front Immunol, 2023). These findings are important as immunopeptide glycosylation remains poorly understood in immunosurveillance. The study also opened up new technical and biological questions that we address in the second part of this communication. Our study highlighted that the performance of the search engines used to detect glycosylated immunopeptides from LC-MS/MS data remains untested and, importantly, that little biochemical in vivo evidence is available to document the nature of glycopeptide antigens in tumour tissues. To this end, we compared the N-glycosylated MHC-II-bound immunopeptides that were reported from tumour tissues of 14 meningioma patients in the MSFragger-HLA-Glyco database (Bedran, Nat Commun, 2023) to those we identified with the commercial Byonic software. Encouragingly, the search engines produced similar outputs supporting that N-glycosylated MHC-II-bound immunopeptides are prevalent in meningioma tumour tissues. Consistent also with in vitro findings, the tissue-derived MHC-II-bound immunopeptides were found to predominantly carry hyper-processed (paucimannosidic- and chitobiose core-type) and hypo-processed (oligomannosidic-type) N-glycans that varied in prevalence and distribution between patients. Taken together, evidence is emerging suggesting that α-mannosidic glycoepitopes abundantly decorate MHC-II-bound immunopeptides presented in both immortalised cells and tumour tissues warranting further research into their functional roles in immunosurveillance.

3D genome contributes to MHC-II neoantigen prediction.

Reliable and ultra-fast DNA and RNA sequencing have been achieved with the emergence of high-throughput sequencing technology. When combining the results of DNA and RNA sequencing for tumor cells of cancer patients, neoantigens that potentially stimulate the immune response of either CD4+ or CD8+ T cells can be identified. However, due to the abundance of somatic mutations and the high polymorphic nature of human leukocyte antigen (HLA) it is challenging to accurately predict the neoantigens. Moreover, comparing to HLA-I presented peptides, the HLA-II presented peptides are more variable in length, making the prediction of HLA-II loaded neoantigens even harder. A number of computational approaches have been proposed to address this issue but none of them considers the DNA origin of the neoantigens from the perspective of 3D genome. Here we investigate the DNA origins of the immune-positive and non-negative HLA-II neoantigens in the context of 3D genome and discovered that the chromatin 3D architecture plays an important role in more effective HLA-II neoantigen prediction. We believe that the 3D genome information will help to increase the precision of HLA-II neoantigen discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Upregulation of HLA-II related to LAG-3+CD4+ T cell infiltration is associated with patient outcome in human glioblastoma.

Glioblastoma (GBM) is the most common malignant diffuse glioma of the brain. Although immunotherapy with immune checkpoint inhibitors (ICIs), such as programmed cell death protein (PD)-1/PD ligand-1 inhibitors, has revolutionized the treatment of several cancers, the clinical benefit in GBM patients has been limited. Lymphocyte-activation gene 3 (LAG-3) binding to human leukocyte antigen-II (HLA-II) plays an essential role in triggering CD4+ T cell exhaustion and could interfere with the efficiency of anti-PD-1 treatment; however, the value of LAG-3-HLA-II interactions in ICI immunotherapy for GBM patients has not yet been analyzed. Therefore, we aimed to investigate the expression and regulation of HLA-II in human GBM samples and the correlation with LAG-3+CD4+ T cell infiltration. Human leukocyte antigen-II was highly expressed in GBM and correlated with increased LAG-3+CD4+ T cell infiltration in the stroma. Additionally, HLA-IIHighLAG-3High was associated with worse patient survival. Increased interleukin-10 (IL-10) expression was observed in GBM, which was correlated with high levels of HLA-II and LAG-3+ T cell infiltration in stroma. HLA-IIHighIL-10High GBM associated with LAG-3+ T cells infiltration synergistically showed shorter overall survival in patients. Combined anti-LAG-3 and anti-IL-10 treatment inhibited tumor growth in a mouse brain GL261 tumor model. In vitro, CD68+ macrophages upregulated HLA-II expression in GBM cells through tumor necrosis factor-α (TNF-α). Blocking TNF-α-dependent inflammation inhibited tumor growth in a mouse GBM model. In summary, T cell-tumor cell interactions, such as LAG-3-HLA-II, could confer an immunosuppressive environment in human GBM, leading to poor prognosis in patients. Therefore, targeting the LAG-3-HLA-II interaction could be beneficial in ICI immunotherapy to improve the clinical outcome of GBM patients.

Epstein-Barr virus-acquired immunodeficiency in myalgic encephalomyelitis-Is it present in long COVID?

Both myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS) and long COVID (LC) are characterized by similar immunological alterations, persistence of chronic viral infection, autoimmunity, chronic inflammatory state, viral reactivation, hypocortisolism, and microclot formation. They also present with similar symptoms such as asthenia, exercise intolerance, sleep disorders, cognitive dysfunction, and neurological and gastrointestinal complaints. In addition, both pathologies present Epstein-Barr virus (EBV) reactivation, indicating the possibility of this virus being the link between both pathologies. Therefore, we propose that latency and recurrent EBV reactivation could generate an acquired immunodeficiency syndrome in three steps: first, an acquired EBV immunodeficiency develops in individuals with "weak" EBV HLA-II haplotypes, which prevents the control of latency I cells. Second, ectopic lymphoid structures with EBV latency form in different tissues (including the CNS), promoting inflammatory responses and further impairment of cell-mediated immunity. Finally, immune exhaustion occurs due to chronic exposure to viral antigens, with consolidation of the disease. In the case of LC, prior to the first step, there is the possibility of previous SARS-CoV-2 infection in individuals with "weak" HLA-II haplotypes against this virus and/or EBV.

HLA class II antibody activation of endothelial cells induces M2 macrophage differentiation in peripheral blood.

BACKGROUND: Donor-specific human leukocyte antigen (HLA) class II antibodies (HLA-II Abs) combined with allogeneic endothelial cells (ECs) mediate high-risk rejection in kidney transplant patients. Macrophage accumulation is a significant histological feature of antibody-mediated rejection (AMR) in kidney transplant patients. Here, we further investigated the effect of HLA-II Abs on macrophage phenotypes to provide theoretical basis for clinical treatment of AMR. METHODS: We prepared an experimental model containing HLA-II Ab-stimulated microvascular ECs and peripheral blood mononuclear cells (PBMCs) co-culture and explored the potential relationship of HLA-II Ab, ECs activation, and macrophage differentiation. Immune phenotype of macrophage subsets was analyzed and quantified by flow cytometry. HLA-II Ab activation of ECs induces M2 macrophage differentiation signal pathways which were investigated by qPCR and western blotting. RESULTS: The stimulation of ECs by F(ab')2 fragment of HLA-II Abs led to phosphorylation of PI3K, Akt, and mTOR, which mediated IL-10, ICAM-1, VCAM-1 secretion. The enhanced ICAM-1 and IL-10 promoted the migration of PBMCs and their differentiation into CD68+ and CD163+ (M2-type) macrophages, respectively, but not CD86+ macrophages. CONCLUSION: These findings revealed the PI3K/Akt/mTOR signal pathways activated by HLA-II Abs in ECs and the immune regulation ability of HLA-II Abs to induce PBMC differentiation.

MS-Based HLA-II Peptidomics Combined With Multiomics Will Aid the Development of Future Immunotherapies.

Immunotherapies have emerged to treat diseases by selectively modulating a patient's immune response. Although the roles of T and B cells in adaptive immunity have been well studied, it remains difficult to select targets for immunotherapeutic strategies. Because human leukocyte antigen class II (HLA-II) peptides activate CD4+ T cells and regulate B cell activation, proliferation, and differentiation, these peptide antigens represent a class of potential immunotherapy targets and biomarkers. To better understand the molecular basis of how HLA-II antigen presentation is involved in disease progression and treatment, systematic HLA-II peptidomics combined with multiomic analyses of diverse cell types in healthy and diseased states is required. For this reason, MS-based innovations that facilitate investigations into the interplay between disease pathologies and the presentation of HLA-II peptides to CD4+ T cells will aid in the development of patient-focused immunotherapies.

HLA Allele-Specific Quantitative Profiling of Type 1 Diabetic B Lymphocyte Immunopeptidome.

Peptide ligands presented by human leukocyte antigen (HLA) molecules on the cell surface represent the immunopeptidome that could be utilized for identification of antigenic peptides for immunotherapy and prevention of autoimmune diseases. Although T-cells are well-known key players in the destruction of pancreatic beta-cells in type 1 diabetes (T1D), increasing evidence points toward a role for B-cells in disease pathogenesis. However, as antigen presenting cells, little is known about the comprehensive immunopeptidome of B cells and their changes in the context of T1D. We performed HLA allele-specific quantitative immunopeptidomics using B lymphocytes derived from T1D patients and healthy controls. Hundreds of HLA-I and HLA-II immunopeptides were identified as differentially regulated in T1D per HLA allele for B cells sharing identical HLA alleles. The results were further validated using additional T1D and healthy B cells with partially overlapped HLA alleles. Differentially expressed immunopeptides were confirmed with targeted proteomics and for reactivity using known T-cell assays in the immune epitope database. Considering samples with identical HLA alleles are difficult to obtain for T1D and other similar HLA-restricted diseases, our work represents a viable approach to better understand HLA allele-specific antigen presentation and may facilitate identification of immunopeptides for therapeutic applications in autoimmune diseases. Data are available via ProteomeXchange with identifier PXD026184.

A genome-wide association study of asthma hospitalizations in adults.

BACKGROUND: Little is known about the genetic determinants of severe asthma exacerbations. OBJECTIVES: We aimed to identify genetic variants associated with asthma hospitalizations. METHODS: We conducted a genome-wide association study of asthma hospitalizations in 34,167 white British adults with asthma, 1,658 of whom had at least 1 asthma-related hospitalization. This analysis was conducted by using logistic regression under an additive genetic model with adjustment for age, sex, body mass index, smoking status, and the first 5 principal components derived from genotypic data. We then analyzed data from 2 cohorts of Latino children and adolescents for replication and conducted quantitative trait locus and functional annotation analyses. RESULTS: At the chromosome 6p21.3 locus, the single-nucleotide polymorphism (SNP) rs56151658 (8 kb from the promoter of HLA-DQB1) was most significantly associated with asthma hospitalizations (for test allele A, odds ratio = 1.36 [95% CI = 1.22-1.52]; P = 3.11 × 10-8); 21 additional SNPs in this locus were associated with asthma hospitalizations at a P value less than 1 × 10-6. In the replication cohorts, multiple SNPs in strong linkage disequilibrium with rs56151658 were associated with severe asthma exacerbations at a P value of .01 or less in the same direction of association as in the discovery cohort. Three HLA genes (HLA-DQA2, HLA-DRB6, and HLA-DOB) were also shown to mediate the estimated effects of the SNPs associated with asthma hospitalizations through effects on gene expression in lung tissue. CONCLUSIONS: We identified strong candidate genes for asthma hospitalizations in adults in the region for class II HLA genes through genomic, quantitative trait locus, and summary data-based mendelian randomization analyses.

Chikungunya Virus Infection Outcome: A Systematic Review of Host Genetics.

Background: Chikungunya virus (CHIKV) is a global concern, inducing chikungunya fever and trigging an arthritogenic chronic phase beyond some severe forms. Outcomes of CHIKV infections in humans are dependent on genetic variations. Here, a systematic review was performed to show evidence of genetic variations on infection outcomes of patients. Methods: Searches were performed in Scopus, SciELO, MEDLINE/PubMed, Web of Science, OneFile (GALE), Periódicos CAPES and ScienceDirect Journals databases. The PICOS approach was used to assess the eligibility of records. A meta-analysis was also conducted to show an association between described alleles/genes and CHIKV infection outcome. Results: Reviews of genetic variants were conducted on genes: CD 209, OAS1, OAS2, OAS3, MIF, TLR-3, TLR-7, TLR-8, MYD-88, KIR, HLA-B; HLA-C; DRB1 and DQB1. Studies were performed on Gabon, Singapore, and India, including Indians, Malay, Gabonese and Chinese ethnicities and published between 2009-2017. The meta-analysis was performed with DRB1 *01; *03; *04; *07; *10; *11; *13; *14 and *15 and DQB1 *02; *03; *05 and *06 alleles with Indian population sample. Sampling power was >80% and a significant positive association between DRB1*14 and CHIKV infection was found (OR = 1.67, 95% CI = 1.04-2.67; p = .03). Conclusion: Majority of the studies were conducted in India. Meta-analysis suggests that DRB1*14 is related to the susceptibility of symptomatic CHIKV infection in Indian population. The literature about CHIKV infection and genetic variations is scarce. The precise role of genetic variation in CHIKV is not clear yet. Further studies are necessary to provide more concrete evidences.

Amino acid encoding for deep learning applications.

BACKGROUND: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction - a process called 'end-to-end learning' - has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. RESULTS: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. CONCLUSION: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deciphering the Structural Enigma of HLA Class-II Binding Peptides for Enhanced Immunoinformatics-based Prediction of Vaccine Epitopes.

Vaccines remain the most efficacious means to avoid and eliminate morbid diseases associated with high morbidity and mortality. Clinical trials indicate the gaining impetus of peptide vaccines against diseases for which an effective treatment still remains obscure. CD4 T-cell-based peptide vaccines involve immunization with antigenic determinants from pathogens or neoplastic cells that possess the ability to elicit a robust T helper cell response, which subsequently activates other arms of the immune system. The available in silico predictors of human leukocyte antigen II (HLA-II) binding peptides are sequence-based techniques, which ostensibly have balanced sensitivity and specificity. Structural analysis and understanding of the cognate peptide and HLA-II interactions are essential to empirically derive a successful peptide vaccine. However, the availability of structure-based epitope prediction algorithms is inadequate compared with sequence-based prediction methods. The present study is an attempt to understand the structural aspects of HLA-II binders by analyzing the Protein Data Bank (PDB) complexes of pHLA-II. Furthermore, we mimic the peptide exchange mechanism and demonstrate the structural implication of an acidic environment on HLA-II binders. Finally, we discuss a structure-guided approach to decipher potential HLA-II binders within an antigenic protein. This strategy may accurately predict the peptide epitopes and thus aid in designing successful peptide vaccines.

Systems Genetics Approaches in Mouse Models of Group A Streptococcal Necrotizing Soft-Tissue Infections.

Mouse models are invaluable resources for studying the pathogenesis and preclinical evaluation of therapeutics and vaccines against many human pathogens. Infections caused by group A streptococcus (GAS, Streptococcus pyogenes) are heterogeneous ranging from mild pharyngitis to severe invasive necrotizing fasciitis, a subgroup of necrotizing soft-tissue infections (NSTIs). While several strains of mice including BALB/c, C3H/HeN, CBA/J, and C57BL/10 offered significant insights, the human specificity and the interindividual variations on susceptibility or resistance to GAS infections limit their ability to mirror responses as seen in humans. In this chapter, we discuss the advanced recombinant inbred (ARI) BXD mouse model that mimics the genetic diversity as seen in humans and underpins the feasibility to map multiple genes (genetic loci) modulating GAS NSTI. GAS produces a myriad of virulence factors, including superantigens (SAg). Superantigens are potent immune toxins that activate T cells by cross-linking T cell receptors with human leukocyte antigen class-II (HLA-II) molecules expressed on antigen-presenting cells. This leads to a pro-inflammatory cytokine storm and the subsequent multiple organ damage and shock. Inbred mice are innately refractive to SAg-mediated responses. In this chapter, we discuss the versatility of the HLA-II transgenic mouse model that allowed the biological validation of known genetic associations to GAS NSTI. The combined utility of ARI-BXD and HLA-II mice as complementary approaches that offer clinically translatable insights into pathomechanisms driven by complex traits and host genetic context and novel means to evaluate the in vivo efficiency of therapies to improve outcomes of GAS NSTI are also discussed.

HLA Class-II Expression in Human Tumors.

HLA class II molecules play a pivotal role in antigen presentation to T lymphocytes. This chapter analyzed the expression of these molecules in different human tumors and their role in cancer progression. The possible connection between tumor HLA class II expression and the pathogenesis of autoimmune diseases is discussed.

Heterogeneity in FoxP3- and GARP/LAP-Expressing T Regulatory Cells in an HLA Class II Transgenic Murine Model of Necrotizing Soft Tissue Infections by Group A Streptococcus.

Invasive group A streptococcus (GAS) infections include necrotizing soft tissue infections (NSTI) and streptococcal toxic shock syndrome (STSS). We have previously shown that host HLA class II allelic variations determine the risk for necrotizing fasciitis (NF), a dominant subgroup of NSTI, and STSS by modulating responses to GAS superantigens (SAgs). SAgs are pivotal mediators of uncontrolled T-cell activation, triggering a proinflammatory cytokine storm in the host. FoxP3-expressing CD4+ CD25+ T regulatory cells (Tregs) comprise phenotypically and functionally heterogeneous subsets with a profound ability to suppress inflammatory responses. Specifically, activated Tregs, which express glycoprotein A repetitions predominant (GARP) and display latent transforming growth factor ß1 (TGF-ß1) complexes (latency-associated peptide [LAP]), exhibit strong immunosuppressive functions. The significance of Tregs that may participate in suppressing inflammatory responses during NSTI is unknown. Here, we phenotypically characterized FoxP3/GARP/LAP-expressing Tregs in GAS-infected or SAg (SmeZ)-stimulated splenocytes from transgenic (tg) mice expressing human HLA-II DRB1*15 (DR15 allele associated with nonsevere NF/STSS-protective responses) or DRB1*0402/DQB1*0302 (DR4/DQ8 alleles associated with neutral risk for combined NF/STSS). We demonstrated both in vivo and in vitro that the neutral-risk allele upregulates expression of CD4+ CD25+ activated effector T cells, with a significantly lower frequency of Foxp3+/GARP+ LAP- but higher frequency of Foxp3- LAP+ Tregs than seen with the protective allele. Additional in vitro studies revealed that the presentation of SmeZ by the neutral-risk allele significantly increases proliferation and expression of effector cytokines gamma interferon (IFN-γ) and interleukin-2 (IL-2) and upregulates CD4+ CD25+ T cell receptors (TCRs) carrying specific Vß 11 chain (TCRVß11+) T cells and Th1 transcription factor Tbx21 mRNA levels. Our data suggest that neutral-risk alleles may drive Th1 differentiation while attenuating the induction of Tregs associated with suppressive function.

[Planar molecular arrangements aid the design of MHC class II binding peptides].

The coupling between peptides and MHC-II proteins in the human immune system is not well understood. This work presents an evidence-based hypothesis of a guiding intermolecular force present in every human MHC-II protein (HLA-II). Previously, we examined the spatial positions of the fully conserved residues in all HLA-II protein types. In each one, constant planar patterns were revealed. These molecular planes comprise of amino acid groups of the same chemical species (for example, Gly) distributed across the protein structure. Each amino acid plane has a unique direction and this directional element offers spatial selectivity. Constant within all planes, too, is the presence of an aromatic residue possessing electrons in movement, leading the authors to consider that the planes generate electromagnetic fields that could serve as an attractive force in a single direction. Selection and attraction between HLA-II molecules and antigen peptides would, therefore, be non-random, resulting in a coupling mechanism as effective and rapid as is clearly required in the immune response. On the basis of planar projections onto the HLA-II groove, modifications were made by substituting the key residues in the class II-associated invariant chain peptide-a peptide with a universal binding affinity-resulting in eight different modified peptides with affinities greater than that of the unmodified peptide. Accurate and reliable prediction of MHC class II-binding peptides may facilitate the design of universal vaccine-peptides with greatly enhanced binding affinities. The proposed mechanisms of selection, attraction and coupling between HLA-II and antigen peptides are explained further in the paper.

From classic to spontaneous and humanized models of multiple sclerosis: impact on understanding pathogenesis and drug development.

Multiple sclerosis (MS), a demyelinating disease of the central nervous system (CNS), presents as a complex disease with variable clinical and pathological manifestations, involving different pathogenic pathways. Animal models, particularly experimental autoimmune encephalomyelitis (EAE), have been key to deciphering the pathophysiology of MS, although no single model can recapitulate the complexity and diversity of MS, or can, to date, integrate the diverse pathogenic pathways. Since the first EAE model was introduced decades ago, multiple classic (induced), spontaneous, and humanized EAE models have been developed, each recapitulating particular aspects of MS pathogenesis. The advances in technologies of genetic ablation and transgenesis in mice of C57BL/6J background and the development of myelin-oligodendrocyte glycoprotein (MOG)-induced EAE in C57BL/6J mice yielded several spontaneous and humanized EAE models, and resulted in a plethora of EAE models in which the role of specific genes or cell populations could be precisely interrogated, towards modeling specific pathways of MS pathogenesis/regulation in MS. Collectively, the numerous studies on the different EAE models contributed immensely to our basic understanding of cellular and molecular pathways in MS pathogenesis as well as to the development of therapeutic agents: several drugs available today as disease modifying treatments were developed from direct studies on EAE models, and many others were tested or validated in EAE. In this review, we discuss the contribution of major classic, spontaneous, and humanized EAE models to our understanding of MS pathophysiology and to insights leading to devising current and future therapies for this disease.

How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred.

MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .

The HLA-II immunopeptidome of SARS-CoV-2.

Targeted synthetic vaccines have the potential to transform our response to viral outbreaks, yet the design of these vaccines requires a comprehensive knowledge of viral immunogens. Here, we report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides that are naturally processed and loaded onto human leukocyte antigen-II (HLA-II) complexes in infected cells. We identify over 500 unique viral peptides from canonical proteins as well as from overlapping internal open reading frames. Most HLA-II peptides colocalize with known CD4+ T cell epitopes in coronavirus disease 2019 patients, including 2 reported immunodominant regions in the SARS-CoV-2 membrane protein. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and nonstructural and noncanonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize vaccine effectiveness.