Major Histocompatibility Complex Alleles Research Articles

The 2024 IPD-MHC database update: a comprehensive resource for major histocompatibility complex studies.

The IPD-MHC Database project (http://www.ebi.ac.uk/ipd/mhc/) serves as a comprehensive and expertly curated repository for major histocompatibility complex (MHC) sequences from non-human species, providing the necessary infrastructure and tools to study the function and evolution of this highly polymorphic genomic region. In its latest version, the IPD-MHC database has expanded both in content and in the tools for data visualization and comparison. The database now hosts over 18 000 MHC alleles from 125 species, organized into eleven taxonomic groups, all manually curated and named by the Comparative MHC Nomenclature Committee. A cetacean section has recently been included, offering researchers valuable data to study the immune system of whales, dolphins, and porpoises, as well establishing the official nomenclature platform for the Cetacea Leukocyte Antigens (CeLA). In response to user demand and reflecting broader trends in bioinformatics and immunogenetics, IPD-MHC now includes the predicted tertiary structure of over 8000 alleles and allows comparison and visualisation of allele variation within and between species at single residue resolution. These latest developments maintain the critically important link between official nomenclature of curated alleles and the ability to analyse this complex polymorphism using the most up to date methods within a single repository.

The Primate Major Histocompatibility Complex: An Illustrative Example of Gene Family Evolution.

Gene families are groups of evolutionarily-related genes. One large gene family that has experienced rapid evolution is the Major Histocompatibility Complex (MHC), whose proteins serve critical roles in innate and adaptive immunity. Across the ~60 million year history of the primates, some MHC genes have turned over completely, some have changed function, some have converged in function, and others have remained essentially unchanged. Past work has typically focused on identifying MHC alleles within particular species or comparing gene content, but more work is needed to understand the overall evolution of the gene family across species. Thus, despite the immunologic importance of the MHC and its peculiar evolutionary history, we lack a complete picture of MHC evolution in the primates. We readdress this question using sequences from dozens of MHC genes and pseudogenes spanning the entire primate order, building a comprehensive set of gene and allele trees with modern methods. Overall, we find that the Class I gene subfamily is evolving much more quickly than the Class II gene subfamily, with the exception of the Class II MHC-DRB genes. We also pay special attention to the often-ignored pseudogenes, which we use to reconstruct different events in the evolution of the Class I region. We find that despite the shared function of the MHC across species, different species employ different genes, haplotypes, and patterns of variation to achieve a successful immune response. Our trees and extensive literature review represent the most comprehensive look into MHC evolution to date.

Model for the Study of Drug Hypersensitivity Based on Bucilamine and the HLA-DRB1*08:02 Allele in Colombian Amerindian Populations

Backgrounds: Allergic diseases and hypersensitivity reactions are common disorders that in turn consist of an extensive genetic component in which the molecules of the Major Histocompatibility Complex (MHC) are included, which have certain alleles associated with the development of hypersensitivity to certain drugs, among which is the allele HLA-DRB1 * 08: 02 as a predisposing factor of hypersensitivity to Bucillamine; this drug is the starting point for the study of the relationship between hypersensitivity reactions to medications and the expression of certain alleles of MHC. Objective: Find the relationship between hypersensitivity to the drug and the expression of the specific allele in Amerindian populations of the Sierra Nevada de Santa Marta and in turn suggest the application of the methodological model proposed in similar studies that seek to relate drug allergies with specific HLA alleles. Methods: A systematic search of information was carried out in the Sience, ScienceDirect, Elsevier and Pubmed databases, the frequencies obtained were tabulated and organized according to their expression to be analyzed with the MEGA7 software. Results: A significant frequency of the HLA-DRB1 * 08: 02 allele was found in the Ijka (61.7%), Arhuaco (41.5%), Kogi (17.9%) and Arsario (15%). Conclusion: A cautious use of Bucillamine and structurally similar drugs it’s recommended in susceptible Amerindian populations, at the same time the application of the proposed model it’s recommended for the study of different drugs that could trigger an allergic reaction based on HLA’s allele expression.

Immunoinformatics design and synthesis of a multi-epitope vaccine against Helicobacter pylori based on lipid nanoparticles

Helicobacter pylori (H. pylori) is responsible for various chronic or acute diseases, such as stomach ulcers, dyspepsia, peptic ulcers, gastroesophageal reflux, gastritis, lymphoma, and stomach cancers. Although specific drugs are available to treat the bacterium's harmful effects, there is an urgent need to develop a preventive or therapeutic vaccine. Therefore, the current study aims to create a multi-epitope vaccine against H. pylori using lipid nanoparticles. Five epitopes from five target proteins of H. pylori, namely, Urease, CagA, HopE, SabA, and BabA, were used. Immunogenicity, MHC (Major Histocompatibility Complex) bonding, allergenicity, toxicity, physicochemical analysis, and global population coverage of the entire epitopes and final construct were carefully examined. The study involved using various bioinformatic web tools to accomplish the following tasks: modeling the three-dimensional structure of a set of epitopes and the final construct and docking them with Toll-Like Receptor 4 (TLR4). In the experimental phase, the final multi-epitope construct was synthesized using the solid phase method, and it was then enclosed in lipid nanoparticles. After synthesizing the construct, its loading, average size distribution, and nanoliposome shape were checked using Nanodrop at 280 nm, dynamic light scattering (DLS), and atomic force microscope (AFM). The designed vaccine has been confirmed to be non-toxic and anti-allergic. It can bind with different MHC alleles at a rate of 99.05%. The construct loading was determined to be about 91%, with an average size of 54 nm. Spherical shapes were also observed in the AFM images. Further laboratory tests are necessary to confirm the safety and immunogenicity of the multi-epitope vaccine.

MRNA vaccine design for Epstein-Barr virus: an immunoinformatic approach.

Epstein-Barr Virus (EBV), structurally similar to other herpes viruses, possess significant global health challenges as it causes infectious mononucleosis and is also associated with various cancers. Due to this widespread impact, an effective messenger RNA (mRNA) vaccine is paramount to help curb its spread, further underscoring the need for its development. This study, following an immunoinformatic approach, aimed to design a comprehensive mRNA vaccine against the EBV by selecting antigenic proteins, predicting Linear B-cell epitopes, cytotoxic T-cell lymphocyte (CTL) and helper T-cell lymphocyte (HTL) epitopes, and assessing vaccine characteristics. Seventy-nine EBV isolates from diverse geographical regions were examined. Additionally, the vaccine construct's physicochemical properties, transmembrane domains, solubility, and secondary structures were analysed. Molecular docking was conducted with Toll-Like Receptor 5 (TLR-5). Population coverage was assessed for selected major histocompatibility complex (MHC) alleles, and immune response was simulated. The result of this study highlighted a vaccine construct with high antigenicity, non-toxicity, and non-allergenicity and possessed favourable physicochemical properties. The vaccine's 3D structure is native-like and strongly binds with TLR-5, indicating a solid affinity with TLR-5. The selected MHC alleles provided broad universal population coverage of 89.1%, and the immune simulations suggested a robust and wide-ranging immunogenic response, activating critical immune cells, antibodies, and cytokines. These findings provide a solid foundation for further development and testing of the EBV candidate vaccine, offering potential solutions for combating EBV infections.

Distribution of Major Histocompatibility Complex Alleles in Cohorts of Patients with Different Levels of Post-­Vaccination Antibodies against Hepatitis B

Relevance. It is known that the immune response to the administration of immunobiological drugs is variable and depends on the individual characteristics of the organism. Host immunogenetic factors have a significant impact on the effectiveness of vaccination. In this study, the frequencies of alleles of the HLA class I (HLA-A, B, C) and class II genes (HLA-DRB1, DPB1, DQB1) were studied in groups of participants with different levels of antibodies (anti-HBs) after vaccination against viral hepatitis B. Aims of the work was to determine the possible relationship between alleles of HLA genes and the intensity of post-vaccination immunity against hepatitis B. Materials and methods. The study included 271 apparently healthy adults who were divided into 3 groups depending on the specific concentration of post-vaccination antibodies (anti-HBs) using ELISA. All calculations were made relative to the groups anti-HBs >100 mIU/ml (n=82), 10-100 mIU/ml (n=98) (protective antibody level) and anti-HBs <10 mIU/ml (n = 91). To type alleles of the HLA class I (HLA-A, B, C) and class II (HLA-DRB1, DPB1, DQB1) genes, we used a panel we developed for whole-genome next-generation sequencing (NGS). Statistical analysis was performed using Pearson's χ2 goodness-of-fit test using the FDR multiple correction method with an initial target of p < 0.05. Results. When typing the six genes studied, the total number of alleles identified at least once was 189 variants that were distinct from each other. We identified 3 alleles (B*38:01:01, DQB1*06:03:01 and DRB1*13:01:01), which were significantly more common (FDR p < 0.05) in the group with a protective level of anti-HBsS. Also in this group there was an increased frequency of occurrence of alleles A*26:01:01, A*32:01:01, C*12:03:01, DPB1*04:01:01 and haplotypes DQB1*06:03:01 -DRB1*13:01:01 and B*38:01:01-C*12:03:01. In the group of seronegative patients, alleles A*02:01:01, A*03:01:01, B*44:02:01, B*44:27:01, C*07:04:01, DPB1*04 were more common :01:01, DQB1*05:01:01, DRB1*01:01:01 and DRB1*16:01:01. It was shown that the identified associations were more significant in the group of individuals with a concentration of post-vaccination anti-HBs above 100 mIU/ml. Conclusion. The results obtained indicate that the HLA alleles we identified may influence the level of anti-HBsS production, and that the genetic factor may, to a greater extent, determine whether the antibody level exceeds 100 mIU/ml. defined as an anti-HBS level of 10 mIU/ ml. The development of an integrated approach to the organization of vaccine prevention, including the determination of genetic markers, will improve the quality of immunization of the population. Information about the association of HLA gene alleles can be used to develop predictive scenarios for the development of the hepatitis B epidemic process.

Strengths and limitations of web servers for the modeling of TCRpMHC complexes

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

Geographic EBV variants confound disease-specific variant interpretation and predict variable immune therapy responses

AbstractEpstein-Barr virus (EBV) is a potent carcinogen linked to hematologic and solid malignancies and causes significant global morbidity and mortality. Therapy using allogeneic EBV-specific lymphocytes shows promise in certain populations, but the impact of EBV genome variation on these strategies remains unexplored. To address this, we sequenced 217 EBV genomes, including hematologic malignancies from Guatemala, Peru, Malawi, and Taiwan, and analyzed them alongside 1307 publicly available EBV genomes from cancer, nonmalignant diseases, and healthy individuals across Africa, Asia, Europe, North America, and South America. These included, to our knowledge, the first natural killer (NK)/T-cell lymphoma (NKTCL) EBV genomes reported outside of East Asia. Our findings indicate that previously proposed EBV genome variants specific to certain cancer types are more closely tied to geographic origin than to cancer histology. This included variants previously reported to be specific to NKTCL but were prevalent in EBV genomes from other cancer types and healthy individuals in East Asia. After controlling for geographic region, we did identify multiple NKTCL-specific variants associated with a 7.8-fold to 21.9-fold increased risk. We also observed frequent variations in EBV genomes that affected peptide sequences previously reported to bind common major histocompatibility complex alleles. Finally, we found several nonsynonymous variants spanning the coding sequences of current vaccine targets BALF4, BKRF2, BLLF1, BXLF2, BZLF1, and BZLF2. These results highlight the need to consider geographic variation in EBV genomes when devising strategies for exploiting adaptive immune responses against EBV-related cancers, ensuring greater global effectiveness and equity in prevention and treatment.

A pan-cetacean MHC amplicon sequencing panel developed and evaluated in combination with genome assemblies.

The major histocompatibility complex (MHC) is a highly polymorphic gene family that is crucial in immunity, and its diversity can be effectively used as a fitness marker for populations. Despite this, MHC remains poorly characterised in non-model species (e.g., cetaceans: whales, dolphins and porpoises) as high gene copy number variation, especially in the fast-evolving class I region, makes analyses of genomic sequences difficult. To date, only small sections of class I and IIa genes have been used to assess functional diversity in cetacean populations. Here, we undertook a systematic characterisation of the MHC class I and IIa regions in available cetacean genomes. We extracted full-length gene sequences to design pan-cetacean primers that amplified the complete exon 2 from MHC class I and IIa genes in one combined sequencing panel. We validated this panel in 19 cetacean species and described 354 alleles for both classes. Furthermore, we identified likely assembly artefacts for many MHC class I assemblies based on the presence of class I genes in the amplicon data compared to missing genes from genomes. Finally, we investigated MHC diversity using the panel in 25 humpback and 30 southern right whales, including four paternity trios for humpback whales. This revealed copy-number variable class I haplotypes in humpback whales, which is likely a common phenomenon across cetaceans. These MHC alleles will form the basis for a cetacean branch of the Immuno-Polymorphism Database (IPD-MHC), a curated resource intended to aid in the systematic compilation of MHC alleles across several species, to support conservation initiatives.

Abstract 5006: Discovery of immunogenic neo-peptides from actionable RNA fusions for developing cancer vaccines

Abstract The global surge in cancer cases, with a staggering 20 million new instances reported in 2023 alone, underscores an urgent demand for advanced and reliable cancer therapeutics. Immunotherapies centered around neoantigens have emerged as a promising avenue for enhancing treatment efficacy and have become a focal point in cancer research. We hypothesize that the tumor cells present a rich neoantigenic repertoire and the immunogenic neoantigens arising from the junctions of proteins translated from chimeric RNAs present viable targets for peptide and mRNA vaccines, offering a promising approach to impede tumor progression. To validate our hypothesis, we acquired a dataset of RNA fusions detected through RNAseq of tissue extracted from patient-derived xenograft (PDX) models established by XenoSTART, a global non clinical oncology contract research organization. This comprehensive dataset encompassed 985 PDX models across 27 cancer types, revealing three fusions associated with the neuregulin-1 (NRG1) gene. NRG1 gene fusions have been identified as a clinically actionable target for multiple solid tumors as they result in ErbB-mediated pathway activation. CD74-NRG1 has emerged as the most frequently reported NRG1 fusion across many cancers such as pancreatic ductal adenocarcinoma, triple negative breast cancer and ovarian cancer. The CD74-NRG1 fusion was identified in two PDX models harboring lung cancer (ST2891 and ST3204) and in an ovarian cancer model (ST088). The lung cancer PDXs displayed a notable abundance of fusion junction-crossing reads. We generated the CD74 (Exon 1-6) -NRG1 (Exon 6-12) fusion transcript model, where the first 6 exons of CD74 fused to exon 6 of NRG1 leading to an in-frame fusion. Validation of this fusion in PDX tissue was accomplished through PCR and Sanger sequencing of the PCR product. We assessed the affinity of the 9-mer neo peptide sequences formed by the translated junction of CD74-NRG1 to Major Histocompatibility Complex (MHC) Class I molecules using the in-silico prediction pipelines MHCNuggets and MixMHCPred-2 and found HLA-A*68:23 to be the MHC allele with highest binding affinity. HLA-Arena was used to investigate the binding affinity between the peptides and the MHC molecules via molecular docking, which revealed 4 peptides to be strong binders to HLA-A*68:23.The immunogenicity of these neoantigenic peptides to HLA-A*68:23 matched Peripheral Blood Mononuclear Cells (PBMCs) will be assessed by the IFN-γ Enzyme Linked Immunosorbent Spot (ELISpot) assay. To elucidate the specific T-cell clonotypes responding to the neo peptides, the 10X Genomics single-cell 5’ Gene Expression Assay, coupled with T Cell Receptor mapping, will be employed. The objective of the study is to establish a robust combinatorial therapeutic strategy leveraging peptide and mRNA vaccine technology to support an effective framework for personalized cancer treatment. Citation Format: Sakuni Rankothgedera, Micah Castillo, Shiyanth Thevasagayampillai, Cole Woody, Aaranyah Kandasamy, Armando Diaz, Michael Wick, Preethi Gunaratne. Discovery of immunogenic neo-peptides from actionable RNA fusions for developing cancer vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5006.

Discovering peptides and computational investigations of a multiepitope vaccine target Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) is the causative agent of tuberculosis (TB), a prevalent airborne infectious disease. Despite the availability of the Bacille Calmette-Guerin vaccine, its global efficacy remains modest, and tuberculosis persists as a significant global public health threat. Addressing this challenge and advancing towards the End MTB Strategy, we developed a multiepitope vaccine (MEV) based on immunoinformatics and computational approaches. Immunoinformatics screening of MBT protein identified immune-dominant epitopes based on Major Histocompatibility Complex (MHC) allele binding, immunogenicity, antigenicity, allergenicity, toxicity, and cytokine inducibility. Selected epitopes were integrated into an MEV construct with adjuvant and linkers, forming a fully immunogenic vaccine candidate. Comprehensive analyses encompassed the evaluation of immunological and physicochemical properties, determination of tertiary structure, molecular docking with Toll-Like Receptors (TLR), molecular dynamics (MD) simulations for all atoms, and immune simulations. Our MEV comprises 534 amino acids, featuring 6 cytotoxic T lymphocyte, 8 helper T lymphocyte, and 7 linear B lymphocyte epitopes, demonstrating high antigenicity and stability. Notably, molecular docking studies and triplicate MD simulations revealed enhanced interactions and stability of MEV with the TLR4 complex compared to TLR2. In addition, the immune simulation indicated the capacity to effectively induce elevated levels of antibodies and cytokines, emphasizing the vaccine's robust immunogenic response. This study presents a promising MEV against TB, exhibiting favorable immunological and physicochemical attributes. The findings provide theoretical support for TB vaccine development. Our study aligns with the global initiative of the End MTB Strategy, emphasizing its potential impact on addressing persistent challenges in TB control.

Building trust in deep learning-based immune response predictors with interpretable explanations

The ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I molecules exist with high levels of accuracy. However, these MHC class I predictors are treated as black-box functions, providing little insight into their decision making. To build turst in these predictors, it is crucial to understand the rationale behind their decisions with human-interpretable explanations. We present MHCXAI, eXplainable AI (XAI) techniques to help interpret the outputs from MHC class I predictors in terms of input peptide features. In our experiments, we explain the outputs of four state-of-the-art MHC class I predictors over a large dataset of peptides and MHC alleles. Additionally, we evaluate the reliability of the explanations by comparing against ground truth and checking their robustness. MHCXAI seeks to increase understanding of deep learning-based predictors in the immune response domain and build trust with validated explanations.

Only rare classical MHC-I alleles are highly expressed in the European house sparrow.

The exceptional polymorphism observed within genes of the major histocompatibility complex (MHC), a core component of the vertebrate immune system, has long fascinated biologists. The highly polymorphic classical MHC class-I (MHC-I) genes are maintained by pathogen-mediated balancing selection (PMBS), as shown by many sites subject to positive selection, while the more monomorphic non-classical MHC-I genes show signatures of purifying selection. In line with PMBS, at any point in time, rare classical MHC alleles are more likely than common classical MHC alleles to confer a selective advantage in host-pathogen interactions. Combining genomic and expression data from the blood of wild house sparrows Passer domesticus, we found that only rare classical MHC-I alleles were highly expressed, while common classical MHC-I alleles were lowly expressed or not expressed. Moreover, highly expressed rare classical MHC-I alleles had more positively selected sites, indicating exposure to stronger PMBS, compared with lowly expressed classical alleles. As predicted, the level of expression was unrelated to allele frequency in the monomorphic non-classical MHC-I alleles. Going beyond previous studies, we offer a fine-scale view of selection on classical MHC-I genes in a wild population by revealing differences in the strength of PMBS according to allele frequency and expression level.

Template-specific optimization of NGS genotyping pipelines reveals allele-specific variation in MHC gene expression.

Using high-throughput sequencing for precise genotyping of multi-locus gene families, such as the major histocompatibility complex (MHC), remains challenging, due to the complexity of the data and difficulties in distinguishing genuine from erroneous variants. Several dedicated genotyping pipelines for data from high-throughput sequencing, such as next-generation sequencing (NGS), have been developed to tackle the ensuing risk of artificially inflated diversity. Here, we thoroughly assess three such multi-locus genotyping pipelines for NGS data, the DOC method, AmpliSAS and ACACIA, using MHC class IIβ data sets of three-spined stickleback gDNA, cDNA and "artificial" plasmid samples with known allelic diversity. We show that genotyping of gDNA and plasmid samples at optimal pipeline parameters was highly accurate and reproducible across methods. However, for cDNA data, the gDNA-optimal parameter configuration yielded decreased overall genotyping precision and consistency between pipelines. Further adjustments of key clustering parameters were required tο account for higher error rates and larger variation in sequencing depth per allele, highlighting the importance of template-specific pipeline optimization for reliable genotyping of multi-locus gene families. Through accurate paired gDNA-cDNA typing and MHC-II haplotype inference, we show that MHC-II allele-specific expression levels correlate negatively with allele number across haplotypes. Lastly, sibship-assisted cDNA-typing of MHC-I revealed novel variants linked in haplotype blocks, and a higher-than-previously-reported individual MHC-I allelic diversity. In conclusion, we provide novel genotyping protocols for the three-spined stickleback MHC-I and -II genes, and evaluate the performance of popular NGS-genotyping pipelines. We also show that fine-tuned genotyping of paired gDNA-cDNA samples facilitates amplification bias-corrected MHC allele expression analysis.

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.

The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements.

T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA.

Identification of neoepitope reactive T-cell receptors guided by HLA-A*03:01 and HLA-A*11:01 immunopeptidomics

BackgroundTumor-specific mutated proteins can create immunogenic non-self, mutation-containing ‘neoepitopes’ that are attractive targets for adoptive T-cell therapies. To avoid the complexity of defining patient-specific, private neoepitopes, there has been major...

Minor histocompatibility antigens represented in HLA-A*02:01 and their search strategies

Minor histocompatibility antigens (MiHAs) are polymorphic peptides on the cell surface derived from self-proteins that are capable to induce an immune response during allogeneic hematopoietic stem cells transplantation. Their presentation occurs in the context of the certain major histocompatibility complex (HLA – human leucocyte antigen) alleles. One of the most common HLA alleles is HLA-A*02:01. Accordingly, for a significant number of donors and recipients pairs, it is possible to use the MiHAs presented in the HLA-A*02:01 as a target for relapsed leukemia therapy. This review discusses the main known MiHAs presented in the context of HLA-A*02:01, their characteristics and approaches used for identification. The described approaches may be used to search for new MiHAs for immunotherapy.

Adaptive immune response selects for postponed maturation and increased body size

Abstract The Major Histocompatibility Complex (MHC) genes encode proteins that initiate the adaptive immune response by presenting pathogen‐derived antigenic peptides to T lymphocytes. Host–pathogen coevolution drives MHC polymorphism, introducing intraspecific variation in host life expectancy. This variation interacts with optimal growth strategy, as growth increases reproductive potential. While mortality rate and body size‐dependent fecundity are major factors shaping life histories, the effect of intraspecific variation in MHC‐based immunity on the evolution of growth strategies and host body size remains unknown. Here, we model how host MHC–pathogen coevolution—and its concomitant impact on host mortality—can affect the evolution of host life histories, as represented by age at maturation and body size. Life histories were compared in scenarios with and without adaptive immune response under equal population‐level mortality rates. We show that host–pathogen coevolutionary dynamics selects for postponed maturation and increased body size. Although MHC genes and genes that determine body size were physically unlinked, selection imposed by the Red Queen process generated linkage disequilibrium between immunocompetent MHC alleles and the maturation‐postponing alleles that prolong growth phase and increase body size. Particularly large body size was attained when pathogens mutated slowly, thus allowing the advantage of resistant MHC alleles to persist over multiple generations. The emergence of adaptive immunity, which is pathogen‐specific and enables immunological memory, is considered a major evolutionary innovation of vertebrates. Our work suggests that the adaptive immune response, mediated by polymorphic MHC genes, may drive the evolution of host body size. This form of adaptive immunity may have thus predisposed vertebrates to evolve large body size and exhibit the macroevolutionary patterns of increasing body size over time that have been detected in comparative studies. Read the free Plain Language Summary for this article on the Journal blog.

Improved haplotype resolution of highly duplicated MHC genes in a long-read genome assembly using MiSeq amplicons.

Long-read sequencing offers a great improvement in the assembly of complex genomic regions, such as the major histocompatibility complex (MHC) region, which can contain both tandemly duplicated MHC genes (paralogs) and high repeat content. The MHC genes have expanded in passerine birds, resulting in numerous MHC paralogs, with relatively high sequence similarity, making the assembly of the MHC region challenging even with long-read sequencing. In addition, MHC genes show rather high sequence divergence between alleles, making diploid-aware assemblers incorrectly classify haplotypes from the same locus as sequences originating from different genomic regions. Consequently, the number of MHC paralogs can easily be over- or underestimated in long-read assemblies. We therefore set out to verify the MHC diversity in an original and a haplotype-purged long-read assembly of one great reed warbler Acrocephalus arundinaceus individual (the focal individual) by using Illumina MiSeq amplicon sequencing. Single exons, representing MHC class I (MHC-I) and class IIB (MHC-IIB) alleles, were sequenced in the focal individual and mapped to the annotated MHC alleles in the original long-read genome assembly. Eighty-four percent of the annotated MHC-I alleles in the original long-read genome assembly were detected using 55% of the amplicon alleles and likewise, 78% of the annotated MHC-IIB alleles were detected using 61% of the amplicon alleles, indicating an incomplete annotation of MHC genes. In the haploid genome assembly, each MHC-IIB gene should be represented by one allele. The parental origin of the MHC-IIB amplicon alleles in the focal individual was determined by sequencing MHC-IIB in its parents. Two of five larger scaffolds, containing 6-19 MHC-IIB paralogs, had a maternal and paternal origin, respectively, as well as a high nucleotide similarity, which suggests that these scaffolds had been incorrectly assigned as belonging to different loci in the genome rather than as alternate haplotypes of the same locus. Therefore, the number of MHC-IIB paralogs was overestimated in the haploid genome assembly. Based on our findings we propose amplicon sequencing as a suitable complement to long-read sequencing for independent validation of the number of paralogs in general and for haplotype inference in multigene families in particular.