Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction

Neoantigen-specific T cell help outperforms non-specific help in multi-antigen DNA vaccination against cancer

CD4+ T helper antigens are essential components of cancer vaccines, but the relevance of the source of these MHC class II-restricted antigens remains underexplored. To compare the effectiveness of tumor-specific versus tumor-unrelated helper antigens, we designed three DNA vaccines for the murine MC-38 colon carcinoma, encoding CD8+ T cell neoantigens alone (noHELP) or in combination with either "universal" helper antigens (uniHELP) or helper neoantigens (neoHELP). Both types of helped vaccines increased the frequency of vaccine-induced CD8+ T cells, and particularly uniHELP increased the fraction of KLRG1+ and PD-1low effector cells. However, when mice were subsequently injected with MC-38 cells, only neoHELP vaccination resulted in significantly better tumor control than noHELP. In contrast to uniHELP, neoHELP-induced tumor control was dependent on the presence of CD4+ T cells, while both vaccines relied on CD8+ T cells. In line with this, neoHELP variants containing wild-type counterparts of the CD4+ or CD8+ T cell neoantigens displayed reduced tumor control. These data indicate that optimal personalized cancer vaccines should include MHC class II-restricted neoantigens to elicit tumor-specific CD4+ T cell help.

Energy landscapes of peptide-MHC binding

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

Yeast-based screening platforms to understand and improve human health

Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.

Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis

INTRODUCTION

Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported.

METHODS

We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database.

RESULTS

We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295.

CONCLUSION

Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

MBE: model-based enrichment estimation and prediction for differential sequencing data

Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. We evaluate MBE using both simulated and real data. Overall, MBE improves accuracy compared to current differential analysis methods.

An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator

The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

Important denominator between autoimmune comorbidities: a review of class II HLA, autoimmune disease, and the gut

Human leukocyte antigen (HLA) genes are associated with more diseases than any other region of the genome. Highly polymorphic HLA genes produce variable haplotypes that are specifically correlated with pathogenically different autoimmunities. Despite differing etiologies, however, many autoimmune disorders share the same risk-associated HLA haplotypes often resulting in comorbidity. This shared risk remains an unanswered question in the field. Yet, several groups have revealed links between gut microbial community composition and autoimmune diseases. Autoimmunity is frequently associated with dysbiosis, resulting in loss of barrier function and permeability of tight junctions, which increases HLA class II expression levels and thus further influences the composition of the gut microbiome. However, autoimmune-risk-associated HLA haplotypes are connected to gut dysbiosis long before autoimmunity even begins. This review evaluates current research on the HLA-microbiome-autoimmunity triplex and proposes that pre-autoimmune bacterial dysbiosis in the gut is an important determinant between autoimmune comorbidities with systemic inflammation as a common denominator.

Evolution by innovation as a driving force to improve TCR-T therapies

Adoptive cell therapies continually evolve through science-based innovation. Specialized innovations for TCR-T therapies are described here that are embedded in an End-to-End Platform for TCR-T Therapy Development which aims to provide solutions for key unmet patient needs by addressing challenges of TCR-T therapy, including selection of target antigens and suitable T cell receptors, generation of TCR-T therapies that provide long term, durable efficacy and safety and development of efficient and scalable production of patient-specific (personalized) TCR-T therapy for solid tumors. Multiple, combinable, innovative technologies are used in a systematic and sequential manner in the development of TCR-T therapies. One group of technologies encompasses product enhancements that enable TCR-T therapies to be safer, more specific and more effective. The second group of technologies addresses development optimization that supports discovery and development processes for TCR-T therapies to be performed more quickly, with higher quality and greater efficiency. Each module incorporates innovations layered onto basic technologies common to the field of immunology. An active approach of "evolution by innovation" supports the overall goal to develop best-in-class TCR-T therapies for treatment of patients with solid cancer.

High-throughput characterization of HLA-E-presented CD94/NKG2x ligands reveals peptides which modulate NK cell activation

HLA-E is a non-classical class I MHC protein involved in innate and adaptive immune recognition. While recent studies have shown HLA-E can present diverse peptides to NK cells and T cells, the HLA-E repertoire recognized by CD94/NKG2x has remained poorly defined, with only a limited number of peptide ligands identified. Here we screen a yeast-displayed peptide library in the context of HLA-E to identify 500 high-confidence unique peptides that bind both HLA-E and CD94/NKG2A or CD94/NKG2C. Utilizing the sequences identified via yeast display selections, we train prediction algorithms and identify human and cytomegalovirus (CMV) proteome-derived, HLA-E-presented peptides capable of binding and signaling through both CD94/NKG2A and CD94/NKG2C. In addition, we identify peptides which selectively activate NKG2C+ NK cells. Taken together, characterization of the HLA-E-binding peptide repertoire and identification of NK activity-modulating peptides present opportunities for studies of NK cell regulation in health and disease, in addition to vaccine and therapeutic design.

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.

TransMHCII: a novel MHC-II binding prediction model built using a protein language model and an image classifier

The emergence of deep learning models such as AlphaFold2 has revolutionized the structure prediction of proteins. Nevertheless, much remains unexplored, especially on how we utilize structure models to predict biological properties. Herein, we present a method using features extracted from protein language models (PLMs) to predict the major histocompatibility complex class II (MHC-II) binding affinity of peptides. Specifically, we evaluated a novel transfer learning approach where the backbone of our model was interchanged with architectures designed for image classification tasks. Features extracted from several PLMs (ESM1b, ProtXLNet or ProtT5-XL-UniRef) were passed into image models (EfficientNet v2b0, EfficientNet v2m or ViT-16). The optimal pairing of the PLM and image classifier resulted in the final model TransMHCII, outperforming NetMHCIIpan 3.2 and NetMHCIIpan 4.0-BA on the receiver operating characteristic area under the curve, balanced accuracy and Jaccard scores. The architecture innovation may facilitate the development of other deep learning models for biological problems.

Yeast display platform with expression of linear peptide epitopes for high-throughput assessment of peptide-MHC-II binding

Yeast display can serve as a powerful tool to assess the binding of peptides to the major histocompatibility complex (pMHC) and pMHC-T-cell receptor binding. However, this approach is often limited by the need to optimize MHC proteins for yeast surface expression, which can be laborious and may not yield productive results. Here we present a second-generation yeast display platform for class II MHC molecules (MHC-II), which decouples MHC-II expression from yeast-expressed peptides, referred to as "peptide display." Peptide display obviates the need for yeast-specific MHC optimizations and increases the scale of MHC-II alleles available for use in yeast display screens. Because MHC identity is separated from the peptide library, a further benefit of this platform is the ability to assess a single library of peptides against any MHC-II. We demonstrate the utility of the peptide display platform across MHC-II proteins, screening HLA-DR, HLA-DP, and HLA-DQ alleles. We further explore parameters of selections, including reagent dependencies, MHC avidity, and use of competitor peptides. In summary, this approach presents an advance in the throughput and accessibility of screening peptide-MHC-II binding.

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.

NIAID workshop on T cell technologies

On 15–16 June 2022, the National Institute of Allergy and Infectious Diseases hosted a virtual workshop on the topic of T cell technologies to discuss assays, novel technology development, bench and clinical application of those technologies, and challenges and innovations in the field.

Autoimmune-associated genetics impact probiotic colonization of the infant gut

To exemplify autoimmune-associated genetic influence on the colonization of bacteria frequently used in probiotics, microbial composition of stool from 1326 one-year-old infants was analyzed in a prospective general-population cohort, All Babies In Southeast Sweden (ABIS). We show that an individual's HLA haplotype composition has a significant impact on which common Bifidobacterium strains thrive in colonizing the gut. The effect HLA has on the gut microbiome can be more clearly observed when considered in terms of allelic dosage. HLA DR1-DQ5 showed the most significant and most prominent effect on increased Bifidobacterium relative abundance. Therefore, HLA DR1-DQ5 is proposed to act as a protective haplotype in many individuals. Protection-associated HLA haplotypes are more likely to influence the promotion of specific bifidobacteria. In addition, strain-level differences are correlated with colonization proficiency in the gut depending on HLA haplotype makeup. These results demonstrate that HLA genetics should be considered when designing effective probiotics, particularly for those at high genetic risk for autoimmune diseases.

T cell Repertoire Profiling and the Mechanism by which HLA-B27 Causes Ankylosing Spondylitis

Purpose of Review

Ankylosing spondylitis (AS) is strongly associated with the HLA-B27 gene. The canonical function of HLA-B27 is to present antigenic peptides to CD8 lymphocytes, leading to adaptive immune responses. The ‘arthritogenic peptide’ theory as to the mechanism by which HLA-B27 induces ankylosing spondylitis proposes that HLA-B27 presents peptides derived from exogenous sources such as bacteria to CD8 lymphocytes, which subsequently cross-react with antigens at the site of inflammation of the disease, causing inflammation. This review describes findings of studies in AS involving profiling of T cell expansions and discusses future research opportunities based on these findings.

Recent Findings

Consistent with this theory, there is an expanding body of data showing that expansion of a restricted pool of CD8 lymphocytes is found in most AS patients yet only in a small proportion of healthy HLA-B27 carriers.

Summary

These exciting findings strongly support the theory that AS is driven by presentation of antigenic peptides to the adaptive immune system by HLA-B27. They point to new potential approaches to identify the exogenous and endogenous antigens involved and to potential therapies for the disease.

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by major histocompatibility complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here, we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high-throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. While the peptide datasets show broad agreement with previously described MHC-binding motifs, they additionally reveal experimentally validated computational false positives and false negatives. We therefore present this approach as able to complement current experimental datasets and computational predictions. Further, our yeast display approach underlines design considerations for epitope identification experiments and serves as a framework for examining relationships between viral conservation and MHC binding, which can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of our approach to determine peptide-MHC binding interactions in a manner that can supplement and potentially enhance current algorithm-based approaches.

Proteome-Scale Screening to Identify High-Expression Signal Peptides with Minimal N-Terminus Biases via Yeast Display

Signal peptides are critical for the efficient expression and routing of extracellular and secreted proteins. Most protein production and screening technologies rely upon a relatively small set of signal peptides. Despite their central role in biotechnology, there are limited studies comprehensively examining the interplay between signal peptides and expressed protein sequences. Here, we describe a high-throughput method to screen novel signal peptides that maintain a high degree of surface expression across a range of protein scaffolds with highly variable N-termini. We find that the canonical signal peptide used in yeast surface display, derived from Aga2p, fails to achieve high surface expression for 42.5% of constructs containing diverse N-termini. To circumvent this, we have identified two novel signal peptides derived from endogenous yeast proteins, SRL1 and KISH, which are highly tolerant to diverse N-terminal sequences. This pipeline can be used to expand our understanding of signal peptide function, identify improved signal peptides for protein expression, and refine the computational tools used for signal peptide prediction.

High-resolution profiling of MHC II peptide presentation capacity reveals SARS-CoV-2 CD4 T cell targets and mechanisms of immune escape

Understanding peptide presentation by specific MHC alleles is fundamental for controlling physiological functions of T cells and harnessing them for therapeutic use. However, commonly used in silico predictions and mass spectroscopy have their limitations in precision, sensitivity, and throughput, particularly for MHC class II. Here, we present MEDi, a novel mammalian epitope display that allows an unbiased, affordable, high-resolution mapping of MHC peptide presentation capacity. Our platform provides a detailed picture by testing every antigen-derived peptide and is scalable to all the MHC II alleles. Given the urgent need to understand immune evasion for formulating effective responses to threats such as SARS-CoV-2, we provide a comprehensive analysis of the presentability of all SARS-CoV-2 peptides in the context of several HLA class II alleles. We show that several mutations arising in viral strains expanding globally resulted in reduced peptide presentability by multiple HLA class II alleles, while some increased it, suggesting alteration of MHC II presentation landscapes as a possible immune escape mechanism.

Comparing Class II MHC DRB3 Diversity in Colombian Simmental and Simbrah Cattle Across Worldwide Bovine Populations

The major histocompatibility complex (MHC) exerts great influence on responses to infectious diseases and vaccination due to its fundamental role in the adaptive immune system. Knowledge about MHC polymorphism distribution among breeds can provide insights into cattle evolution and diversification as well as population-based immune response variability, thus guiding further studies. Colombian Simmental and Simbrah cattle’s BoLA-DRB3 genetic diversity was compared to that of taurine and zebuine breeds worldwide to estimate functional diversity. High allele richness was observed for Simmental and Simbrah cattle; nevertheless, high homozygosity was associated with individual low sequence variability in both the β1 domain and the peptide binding region (PBR), thereby implying reduced MHC-presented peptide repertoire size. There were strong signals of positive selection acting on BoLA-DRB3 in all populations, some of which were poorly structured and displayed common alleles accounting for their high genetic similarity. PBR sequence correlation analysis suggested that, except for a few populations exhibiting some divergence at PBR, global diversity regarding potential MHC-presented peptide repertoire could be similar for the cattle populations analyzed here, which points to the retention of functional diversity in spite of the selective pressures imposed by breeding.

V-CARMA: A tool for the detection and modification of antigen-specific T cells

Immunotherapy is a promising approach to treat cancers, infectious diseases, and autoimmunity by harnessing the power of immune cells, especially T cells. To improve the precision and efficacy of immunotherapy, the ability to genetically modify antigen-specific T cells is needed but cannot be accomplished using current methods. Here, we present a method, V-CARMA (Viral ChimAeric Receptor MHC-Antigen), to generate lentiviruses displaying peptide-MHC complex to specifically target T cells that express cognate TCRs and subsequently deliver genes into target T cells for genetic modification. Our results demonstrate that V-CARMA is a versatile tool to detect and modify antigen-specific T cells.

T cells promote our body’s ability to battle cancers and infectious diseases but can act pathologically in autoimmunity. The recognition of peptides presented by major histocompatibility complex (pMHC) molecules by T cell receptors (TCRs) enables T cell–mediated responses. To modify disease-relevant T cells, new tools to genetically modify T cells and decode their antigen recognition are needed. Here, we present an approach using viruses pseudotyped with peptides loaded on MHC called V-CARMA (Viral ChimAeric Receptor MHC-Antigen) to specifically target T cells expressing cognate TCRs for antigen discovery and T cell engineering. We show that lentiviruses displaying antigens on human leukocyte antigen (HLA) class I and class II molecules can robustly infect CD8⁺ and CD4⁺ T cells expressing cognate TCRs, respectively. The infection rates of the pseudotyped lentiviruses (PLVs) are correlated with the binding affinity of the TCR to its cognate antigen. Furthermore, peptide-HLA pseudotyped lentivirus V-CARMA constructs can identify target cells from a mixed T cell population, suppress PD-1 expression on CD8⁺ T cells via PDCD1 shRNA delivery, and induce apoptosis in autoreactive CD4⁺ T cells. Thus, V-CARMA is a versatile tool for TCR ligand identification and selective T cell manipulation.

Comprehensive Analysis of CD4+ T Cell Response Cross-Reactive to SARS-CoV-2 Antigens at the Single Allele Level of HLA Class II

Common human coronaviruses have been circulating undiagnosed worldwide. These common human coronaviruses share partial sequence homology with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); therefore, T cells specific to human coronaviruses are also cross-reactive with SARS-CoV-2 antigens. Herein, we defined CD4⁺ T cell responses that were cross-reactive with SARS-CoV-2 antigens in blood collected in 2016–2018 from healthy donors at the single allele level using artificial antigen-presenting cells (aAPC) expressing a single HLA class II allotype. We assessed the allotype-restricted responses in the 42 individuals using the aAPCs matched 22 HLA-DR alleles, 19 HLA-DQ alleles, and 13 HLA-DP alleles. The response restricted by the HLA-DR locus showed the highest magnitude, and that by HLA-DP locus was higher than that by HLA-DQ locus. Since two alleles of HLA-DR, -DQ, and -DP loci are expressed co-dominantly in an individual, six different HLA class II allotypes can be used to the cross-reactive T cell response. Of the 16 individuals who showed a dominant T cell response, five, one, and ten showed a dominant response by a single allotype of HLA-DR, -DQ, and -DP, respectively. The single allotype-restricted T cells responded to only one antigen in the five individuals and all the spike, membrane, and nucleocapsid proteins in the six individuals. In individuals heterozygous for the HLA-DPA and HLA-DPB loci, four combinations of HLA-DP can be expressed, but only one combination showed a dominant response. These findings demonstrate that cross-reactive T cells to SARS-CoV-2 respond with single-allotype dominance.

Yeast Surface Display: New Opportunities for a Time-Tested Protein Engineering System

Yeast surface display has proven to be a powerful tool for the discovery of antibodies and other novel binding proteins and for engineering the affinity and selectivity of existing proteins for their targets. In the decades since the first demonstrations of the approach, the range of yeast display applications has greatly expanded to include many different protein targets and has grown to encompass methods for rapid protein characterization. Here, we briefly summarize the development of yeast display methodologies and highlight several selected examples of recent applications to timely and challenging protein engineering and characterization problems.

Yeast Surface Display for Protein Engineering: Library Generation, Screening, and Affinity Maturation

Yeast surface display is a powerful directed evolution method for developing and engineering protein molecules to attain desired properties. Here, updated protocols are presented for purposes of identification of lead binders and their affinity maturation. Large libraries are screened by magnetic bead selections followed by flow cytometric selections. Upon identification and characterization of single clones, their affinities are improved by an iterative process of mutagenesis and fluorescence-activated cell sorting.

Yeast Display for the Identification of Peptide-MHC Ligands of Immune Receptors

T cells detect peptide antigens presented by major histocompatibility complex (MHC) proteins via their T cell receptor (TCR). The sequence diversity of possible antigens, with trillions of potential peptide-MHC targets, makes it challenging to study, characterize, and manipulate the peptide repertoire of a given TCR. Yeast display has been utilized to study the interactions between peptide-MHCs and T cell receptors to facilitate high-throughput screening of peptide-MHC libraries. Here we present insights on designing and validating a peptide-MHC yeast display construct, designing and constructing peptide libraries, conducting selections, and preparing, processing, and analyzing peptide library sequencing data. Applications for this approach are broad, including characterizing peptide-MHC recognition profiles for a TCR, screening for high-affinity mimotopes of known TCR-binding peptides, and identifying natural ligands of TCRs from expanded T cells.

Contribution of HLA class II genes, DRB4*01:01, DRB1*07:01, and DQB1*03:03:2 to clinical features of Vitiligo disease in Iranian population

BACKGROUND

Vitiligo is a multifactorial depigmentation condition, which is due to skin melanocyte destruction. Increased expression of HLA class II genes in patients with pre-lesions of Vitiligo suggests a crucial role for the participation of immune response in Vitiligo development. Recent studies progressively focused on HLA-DRB1 and DQB1 genes. In this study, we have evaluated the association and role of HLA-DRB4*01:01, -DRB1*07:01, and -DQB1*03:03:2 genes in different clinical subtypes of Vitiligo in the Iranian population.

METHODS

First, Genomic DNA from peripheral blood of 125 unrelated Vitiligo patients and 100 unrelated healthy controls were extracted through the salting-out method. Then, HLA class II genotyping was performed using the sequence-specific primer PCR method. Finally, the clinical relevance of the testing for these genotypes was evaluated by applying the PcPPV (prevalence-corrected positive predictive value) formula.

RESULTS

Our results indicated the positive associations of DRB4*01:01 and DRB1*07:01 allelic genes with early-onset Vitiligo (p = 0.024 and 0.022, respectively). DRB4*01:01 also showed strong protection against late-onset Vitiligo (p = 0.0016, RR = 0.360). Moreover, our data revealed that the DRB1*07:01 increases the susceptibility to Sporadic Vitiligo (p = 0.030, RR = 1.702). Furthermore, our findings proposed that elevated vulnerability of Vitiligo patients due to DRB4*01:01 and DRB1*07:01 alleles maybe is correlated with the presence of amino acid Arginine at position 71 at pocket 4 on the antigen-binding site of the HLA-DRB1 receptor.

CONCLUSION

Our findings on different subtypes of Vitiligo suggest that, despite a more apparent autoimmune involvement, a non-autoimmune nature for the etiology of Vitiligo should also be considered.

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

Yeast display of MHC-II enables rapid identification of peptide ligands from protein antigens (RIPPA)

CD4+ T cells orchestrate adaptive immune responses via binding of antigens to their receptors through specific peptide/MHC-II complexes. To study these responses, it is essential to identify protein-derived MHC-II peptide ligands that constitute epitopes for T cell recognition. However, generating cells expressing single MHC-II alleles and isolating these proteins for use in peptide elution or binding studies is time consuming. Here, we express human MHC alleles (HLA-DR4 and HLA-DQ6) as native, noncovalent αβ dimers on yeast cells for direct flow cytometry-based screening of peptide ligands from selected antigens. We demonstrate rapid, accurate identification of DQ6 ligands from pre-pro-hypocretin, a narcolepsy-related immunogenic target. We also identify 20 DR4-binding SARS-CoV-2 spike peptides homologous to SARS-CoV-1 epitopes, and one spike peptide overlapping with the reported SARS-CoV-2 epitope recognized by CD4+ T cells from unexposed individuals carrying DR4 subtypes. Our method is optimized for immediate application upon the emergence of novel pathogens.

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.

Major histocompatibility complex (MHC) class I and class II proteins: impact of polymorphism on antigen presentation

The major histocompatibility complex (MHC) loci are amongst the most polymorphic regions in the genomes of vertebrates. In the human population, thousands of MHC gene variants (alleles) exist that translate into distinct allotypes equipped with overlapping but unique peptide binding profiles. Understanding the differential structural and dynamic properties of MHC alleles and their interaction with critical regulators of peptide exchange bears the potential for more personalized strategies of immune modulation in the context of HLA-associated diseases.

Machine learning optimization of peptides for presentation by class II MHCs

T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by MHC-presented antigens contained in peptide vaccines. We present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying their anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization improves peptide binding. Supplementary information: Supplementary data are available at Bioinformatics online.

Antigen discovery tools for adaptive immune receptor repertoire research

The adaptive immune system has evolved to recognize with incredible precision a large diversity of molecules. Innovations in high-throughput sequencing and bioinformatics have accelerated large-scale immune repertoire analyses and given us important insights into the behavior of the adaptive immune system. However, establishing a connection between receptor sequence and its antigen-specificity remains a challenge despite its central role in determining T and B cell fate. We discuss recent large-scale antigen discovery technologies which can be combined with adaptive immune receptor repertoire (AIRR) studies. We highlight important discoveries made using repertoire analyses in the field of host-microbe interactions.