Understanding the drivers of MHC restriction of T cell receptors

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

Preparation empty peptide-receptive MHC class I complex for large-scale detection through photolabile peptide ligands

Peptide-major histocompatibility complex (pMHC) multimers are wide recognized as the premier technique for detecting, characterizing, and isolating antigen-specific CD8+ T-cell subsets. These multimers are specifically useful in studying infections, autoimmune conditions, and cancer through single-cell analysis techniques such as flow cytometry and fluorescence microscopy. However, the development of high-throughput assays with commercially available pMHC tetramers can be expensive, while in-house production may pose challenges for most biology research laboratories. In this context, we introduce a cost-friendly and uncomplicated protocol to prepare empty MHC class I tetramers using disulfide-stabilized molecules and photolabile peptide ligands. Our method relies on disulfide bond-stabilized MHC-I molecules, which demonstrated stability when folded into stable monomers in the presence of a photolabile epitope. These monomers, upon ultraviolet irradiation and streptavidin binding, efficiently assemble into tetramers devoid of any peptide. Following a short incubation with the peptide of interest under gentle conditions, the resulting pMHC tetramer effectively detects patient-sourced, neoantigen-specific T cells. Our unique approach streamlines large-scale pMHC generation, thus paving the way for advancements in T cell-based diagnostics and personalized therapies.

SARS-CoV-2 envelope protein-derived extracellular vesicles act as potential media for viral spillover

Extracellular vesicles (EVs) are shown to be a novel viral transmission model capable of increasing a virus's tropism. According to our earlier research, cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or transfected with envelope protein plasmids generate a novel type of EVs that are micrometer-sized and able to encase virus particles. Here, we showed the capacity of these EVs to invade various animals both in vitro and in vivo independent of the angiotensin-converting enzyme 2 receptor. First, via macropinocytosis, intact EVs produced from Vero E6 (monkey) cells were able to enter cells from a variety of animals, including cats, dogs, bats, hamsters, and minks, and vice versa. Second, when given to zebrafish with cutaneous wounds, the EVs showed favorable stability in aqueous environments and entered the fish. Moreover, infection of wild-type (WT) mice with heterogeneous EVs carrying SARS-CoV-2 particles led to a strong cytokine response and a notable amount of lung damage. Conversely, free viral particles did not infect WT mice. These results highlight the variety of processes behind viral transmission and cross-species evolution by indicating that EVs may be possible vehicles for SARS-CoV-2 spillover and raising risk concerns over EVs' potential for viral gene transfer.

New immune cell engagers for cancer immunotherapy

There have been major advances in the immunotherapy of cancer in recent years, including the development of T cell engagers - antibodies engineered to redirect T cells to recognize and kill cancer cells - for the treatment of haematological malignancies. However, the field still faces several challenges to develop agents that are consistently effective in a majority of patients and cancer types, such as optimizing drug dose, overcoming treatment resistance and improving efficacy in solid tumours. A new generation of T cell-targeted molecules was developed to tackle these issues that are potentially more effective and safer. In addition, agents designed to engage the antitumour activities of other immune cells, including natural killer cells and myeloid cells, are showing promise and have the potential to treat a broader range of cancers.

A structure-guided approach to predict MHC-I restriction of T cell receptors for public antigens

Peptides presented by class I major histocompatibility complex (MHC-I) proteins provide biomarkers for therapeutic targeting using T cell receptors (TCRs), TCR-mimicking antibodies (TMAs), or other engineered protein binders. Despite the extreme sequence diversity of the Human Leucocyte Antigen (HLA, the human MHC), a given TCR or TMA is restricted to recognize epitopic peptides in the context of a limited set of different HLA allotypes. Here, guided by our analysis of 96 TCR:pHLA complex structures in the Protein Data Bank (PDB), we identify TCR contact residues and classify 148 common HLA allotypes into T-cell cross-reactivity groups (T-CREGs) on the basis of their interaction surface features. Insights from our work have actionable value for resolving MHC-I restriction of TCRs, guiding therapeutic expansion of existing therapies, and informing the selection of peptide targets for forthcoming immunotherapy modalities.

Novel insights into the immune response to bacterial T cell superantigens

Bacterial T cell superantigens (SAgs) are a family of microbial exotoxins that function to activate large numbers of T cells simultaneously. SAgs activate T cells by direct binding and crosslinking of the lateral regions of MHC class II molecules on antigen-presenting cells with T cell receptors (TCRs) on T cells; these interactions alter the normal TCR-peptide-MHC class II architecture to activate T cells in a manner that is independent of the antigen specificity of the TCR. SAgs have well-recognized, central roles in human diseases such as toxic shock syndrome and scarlet fever through their quantitative effects on the T cell response; in addition, numerous other consequences of SAg-driven T cell activation are now being recognized, including direct roles in the pathogenesis of endocarditis, bloodstream infections, skin disease and pharyngitis. In this Review, we summarize the expanding family of bacterial SAgs and how these toxins can engage highly diverse adaptive immune receptors. We highlight recent findings regarding how SAg-driven manipulation of the adaptive immune response may operate in multiple human diseases, as well as contributing to the biology and life cycle of SAg-producing bacterial pathogens.

RACER-m leverages structural features for sparse T cell specificity prediction

Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.

Helper T cell immunity in humans with inherited CD4 deficiency

CD4+ T cells are vital for host defense and immune regulation. However, the fundamental role of CD4 itself remains enigmatic. We report seven patients aged 5-61 years from five families of four ancestries with autosomal recessive CD4 deficiency and a range of infections, including recalcitrant warts and Whipple's disease. All patients are homozygous for rare deleterious CD4 variants impacting expression of the canonical CD4 isoform. A shorter expressed isoform that interacts with LCK, but not HLA class II, is affected by only one variant. All patients lack CD4+ T cells and have increased numbers of TCRαβ+CD4-CD8- T cells, which phenotypically and transcriptionally resemble conventional Th cells. Finally, patient CD4-CD8- αβ T cells exhibit intact responses to HLA class II-restricted antigens and promote B cell differentiation in vitro. Thus, compensatory development of Th cells enables patients with inherited CD4 deficiency to acquire effective cellular and humoral immunity against an unexpectedly large range of pathogens. Nevertheless, CD4 is indispensable for protective immunity against at least human papillomaviruses and Trophyrema whipplei.

Adoptive cell therapy for solid tumors beyond CAR-T: Current challenges and emerging therapeutic advances

Adoptive cellular immunotherapy using immune cells expressing chimeric antigen receptors (CARs) is a highly specific anti-tumor immunotherapy that has shown promise in the treatment of hematological malignancies. However, there has been a slow progress toward the treatment of solid tumors owing to the complex tumor microenvironment that affects the localization and killing ability of the CAR cells. Solid tumors with a strong immunosuppressive microenvironment and complex vascular system are unaffected by CAR cell infiltration and attack. To improve their efficacy toward solid tumors, CAR cells have been modified and upgraded by "decorating" and "pruning". This review focuses on the structure and function of CARs, the immune cells that can be engineered by CARs and the transformation strategies to overcome solid tumors, with a view to broadening ideas for the better application of CAR cell therapy for the treatment of solid tumors.

Pathways and mechanisms of CD4+CD8αα+ intraepithelial T cell development

The mammalian small intestine epithelium harbors a peculiar population of CD4+CD8αα+ T cells that are derived from mature CD4+ T cells through reprogramming of lineage-specific transcription factors. CD4+CD8αα+ T cells occupy a unique niche in T cell biology because they exhibit mixed phenotypes and functional characteristics of both CD4+ helper and CD8+ cytotoxic T cells. The molecular pathways driving their generation are not fully mapped. However, recent studies demonstrate the unique role of the commensal gut microbiota as well as distinct cytokine and chemokine requirements in the differentiation and survival of these cells. We review the established and newly identified factors involved in the generation of CD4+CD8αα+ intraepithelial lymphocytes (IELs) and place them in the context of the molecular machinery that drives their phenotypic and functional differentiation.

Deciphering Membrane-Protein Interactions and High-Throughput Antigen Identification with Cell Doublets

Deciphering cellular interactions is essential to both understand the mechanisms underlying a broad range of human diseases, but also to manipulate therapies targeting these diseases. Here, the formation of cell doublets resulting from specific membrane ligand-receptor interactions is discovered. Based on this phenomenon, the study developed DoubletSeeker, a novel high-throughput method for the reliable identification of ligand-receptor interactions. The study shows that DoubletSeeker can accurately identify T cell receptor (TCR)-antigen interactions with high sensitivity and specificity. Notably, DoubletSeeker effectively captured paired TCR-peptide major histocompatibility complex (pMHC) information during a highly complex library-on-library screening and successfully identified three mutant TCRs that specifically recognize the MART-1 epitope. In turn, DoubletSeeker can act as an antigen discovery platform that allows for the development of novel immunotherapy targets, making it valuable for investigating fundamental tumor immunology.

Revolutionizing cancer treatment: the power of bi- and tri-specific T-cell engagers in oncolytic virotherapy

Bi- or tri-specific T cell engagers (BiTE or TriTE) are recombinant bispecific proteins designed to stimulate T-cell immunity directly, bypassing antigen presentation by antigen-presenting cells (APCs). However, these molecules suffer from limitations such as short biological half-life and poor residence time in the tumor microenvironment (TME). Fortunately, these challenges can be overcome when combined with OVs. Various strategies have been developed, such as encoding secretory BiTEs within OV vectors, resulting in improved targeting and activation of T cells, secretion of key cytokines, and bystander killing of tumor cells. Additionally, oncolytic viruses armed with BiTEs have shown promising outcomes in enhancing major histocompatibility complex I antigen (MHC-I) presentation, T-cell proliferation, activation, and cytotoxicity against tumor cells. These combined approaches address tumor heterogeneity, drug delivery, and T-cell infiltration, offering a comprehensive and effective solution. This review article aims to provide a comprehensive overview of Bi- or TriTEs and OVs as promising therapeutic approaches in the field of cancer treatment. We summarize the cutting-edge advancements in oncolytic virotherapy immune-related genetic engineering, focusing on the innovative combination of BiTE or TriTE with OVs.

copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling

T cell receptor (TCR) repertoire diversity enables the orchestration of antigen-specific immune responses against the vast space of possible pathogens. Identifying TCR/antigen binding pairs from the large TCR repertoire and antigen space is crucial for biomedical research. Here, we introduce copepodTCR, an open-access tool for the design and interpretation of high-throughput experimental assays to determine TCR specificity. copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, useful for "deorphaning" TCRs of unknown specificity. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased results interpretation, identifies the response-eliciting peptide for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up. We experimentally validated our approach on a library of 253 overlapping peptides covering the SARS-CoV-2 spike protein. We provide experimental guides for efficient design of larger screens covering thousands of peptides which will be crucial for the identification of antigen-specific T cells and their targets from limited clinical material.

Rapid and high throughput assessment of cellular immunity against SARS-CoV-2 based on the ex vivo activation of genes in leukocyte assay with whole blood

During the novel coronavirus outbreak and vaccine development, antibody production garnered major focus as the primary immunogenic response. However, cellular immunity's recent demonstration of comparable or greater significance in controlling infection demands the re-evaluation of the importance of T-cell immunity in SARS-CoV-2 infection. Here, we developed a novel assay, the ex vivo activation of genes in leukocytes (EAGL), which employs short-term whole blood stimulation with the LeukoComplete™ system, to measure ex vivo SARS-CoV-2-specific T cell responses (cellular immunity). This assay measures upregulated mRNA expression related to leukocyte activation 4 h after antigen stimulation. LeukoComplete™ system uses whole blood samples, eliminating the need for pretreatment before analysis. Furthermore, this system's high reproducibility is ensured through a series of operations from mRNA extraction to cDNA synthesis on a 96-well plate. In the performance evaluation using fresh blood from previously SARS-CoV-2-infected and COVID-19-vaccinated individuals, the EAGL assay had a comparable sensitivity and specificity to the ELISpot assay (EAGL: 1.000/1.000; ELISpot: 0.900/0.973). As a simple, high-throughput assay, the EAGL assay is also a quantitative test that is useful in studies with large sample numbers, such as monitoring new vaccine efficacies against novel coronaviruses or epidemiologic studies that require cellular immune testing during viral infection.

Current Views about the Inflammatory Damage Triggered by Bacterial Superantigens and Experimental Attempts to Neutralize Superantigen-Mediated Toxic Effects with Natural and Biological Products

Superantigens, i.e., staphylococcal enterotoxins and toxic shock syndrome toxin-1, interact with T cells in a different manner in comparison to conventional antigens. In fact, they activate a larger contingent of T lymphocytes, binding outside the peptide-binding groove of the major histocompatibility complex class II. Involvement of many T cells by superantigens leads to a massive release of pro-inflammatory cytokines, such as interleukin (IL)-1, IL-2, IL-6, tumor necrosis factor-alpha and interferon-gamma. Such a storm of mediators has been shown to account for tissue damage, multiorgan failure and shock. Besides conventional drugs and biotherapeutics, experiments with natural and biological products have been undertaken to attenuate the toxic effects exerted by superantigens. In this review, emphasis will be placed on polyphenols, probiotics, beta-glucans and antimicrobial peptides. In fact, these substances share a common functional denominator, since they skew the immune response toward an anti-inflammatory profile, thus mitigating the cytokine wave evoked by superantigens. However, clinical applications of these products are still scarce, and more trials are needed to validate their usefulness in humans.

Evolutionary trade-offs constraining the MHC gene expansion: beyond simple TCR depletion model

The immune system is as much shaped by the pressure of pathogens as it is by evolutionary trade-offs that constrain its structure and function. A perfect example comes from the major histocompatibility complex (MHC), molecules that initiate adaptive immune response by presentation of foreign antigens to T cells. The remarkable, population-level polymorphism of MHC genes is assumed to result mainly from a co-evolutionary arms race between hosts and pathogens, while the limited, within-individual number of functional MHC loci is thought to be the consequence of an evolutionary trade-off between enhanced pathogen recognition and excessive T cell depletion during negative selection in the thymus. Certain mathematical models and infection studies suggest that an intermediate individual MHC diversity would thus be optimal. A recent, more direct test of this hypothesis has shown that the effects of MHC diversity on T-cell receptor (TCR) repertoires may differ between MHC classes, supporting the depletion model only for MHC class I. Here, we used the bank vole (Myodes=Cletronomys glareolus), a rodent species with variable numbers of expressed MHC genes, to test how an individual MHC diversity influences the proportions and TCR repertoires of responding T cell subsets. We found a non-linear relationship between MHC diversity and T cell proportions (with intermediate MHC numbers coinciding with the largest T cell proportions), perhaps reflecting an optimality effect of balanced positive and negative thymic selection. The association was strongest for the relationship between MHC class I and splenic CD8+ T cells. The CD8+ TCR richness alone was unaffected by MHC class I diversity, suggesting that MHC class I expansion may be limited by decreasing T cell counts, rather than by direct depletion of TCR richness. In contrast, CD4+ TCR richness was positively correlated with MHC class II diversity, arguing against a universal TCR depletion. It also suggests that different evolutionary forces or trade-offs may limit the within-individual expansion of the MHC class II loci.

An mRNA vaccine encoding the SARS-CoV-2 receptor-binding domain protects mice from various Omicron variants

Here, we assessed the efficacy of a lipid nanoparticle-based mRNA vaccine candidate encoding the receptor-binding domain (LNP-mRNA-RBD) in mice. Mice immunized with LNP-mRNA-RBD based on the ancestral strain (ancestral-type LNP-mRNA-RBD) showed similar cellular responses against the ancestral strain and BA.5, but their neutralizing activity against BA.5 was lower than that against the ancestral strain. The ancestral-type LNP-mRNA-RBD protected mice from the ancestral strain or BA.5 challenge; however, its ability to reduce the viral burdens after BA.5 challenge was limited. In contrast, immunization with bivalent LNP-mRNA-RBD consisting of the ancestral-type and BA.4/5-type LNP-mRNA-RBD or monovalent BA.4/5-type LNP-mRNA-RBD elicited robust cellular responses, as well as high and moderate neutralizing titers against BA.5 and XBB.1.5, respectively. Furthermore, the vaccines containing BA.4/5-type LNP-mRNA-RBD remarkably reduced the viral burdens following BA.5 or XBB.1.5 challenge. Overall, our findings suggest that LNP-mRNA-RBD is effective against SARS-CoV-2 infection.

HLA Genetics for the Human Diseases

Highly polymorphic human leukocyte antigen (HLA) molecules (alleles) expressed by different classical HLA class I and class II genes have crucial roles in the regulation of innate and adaptive immune responses, transplant rejection and in the pathogenesis of numerous infectious and autoimmune diseases. To date, over 35,000 HLA alleles have been published from the IPD-IMGT/HLA database, and specific HLA alleles and HLA haplotypes have been reported to be associated with more than 100 different diseases and phenotypes. Next generation sequencing (NGS) technology developed in recent years has provided breakthroughs in various HLA genomic/gene studies and transplant medicine. In this chapter, we review the current information on the HLA genomic structure and polymorphisms, as well as the genetic context in which numerous disease associations have been identified in this region.

How to Test Human CAR T Cells in Solid Tumors, the Next Frontier of CAR T Cell Therapy

Chimeric antigen receptor (CAR) T cell therapy has proven to be a successful treatment option for leukemias and lymphomas. These encouraging outcomes underscore the potential of adoptive cell therapy for other oncology applications, namely, solid tumors. However, CAR T cells are yet to succeed in treating solid tumors. Unlike liquid tumors, solid tumors create a hostile tumor microenvironment (TME). CAR T cells must traffic to the TME, survive, and retain their function to eradicate the tumor. Nevertheless, there is no universal preclinical model to systematically test candidate CARs and CAR targets for their capacity to infiltrate and eliminate human solid tumors in vivo. Here, we provide a detailed protocol to evaluate human CAR CD4+ helper T cells and CD8+ cytotoxic T cells in immunodeficient (NSG) mice bearing antigen-expressing human solid tumors.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

Chimeric Antigen Receptor T Cell Therapy in Acute Myeloid Leukemia: Trials and Tribulations

Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy that is often associated with relapse and drug resistance after standard chemotherapy or targeted therapy, particularly in older patients. Hematopoietic stem cell transplants are looked upon as the ultimate salvage option with curative intent. Adoptive cell therapy using chimeric antigen receptors (CAR) has shown promise in B cell malignancies and is now being investigated in AML. Initial clinical trials have been disappointing in AML, and we review current strategies to improve efficacy for CAR approaches. The extensive number of clinical trials targeting different antigens likely reflects the genetic heterogeneity of AML. The limited number of patients reported in multiple early clinical studies makes it difficult to draw conclusions about CAR safety, but it does suggest that the efficacy of this approach in AML lags behind the success observed in B cell malignancies. There is a clear need not only to improve CAR design but also to identify targets in AML that show limited expression in normal myeloid lineage cells.

KRAS G12V neoantigen specific T cell receptor for adoptive T cell therapy against tumors

KRAS mutations are broadly recognized as promising targets for tumor therapy. T cell receptors (TCRs) can specifically recognize KRAS mutant neoantigens presented by human lymphocyte antigen (HLA) and mediate T cell responses to eliminate tumor cells. In the present study, we identify two TCRs specific for the 9-mer KRAS-G12V mutant neoantigen in the context of HLA-A*11:01. The TCR-T cells are constructed and display cytokine secretion and cytotoxicity upon co-culturing with varied tumor cells expressing the KRAS-G12V mutation. Moreover, 1-2C TCR-T cells show anti-tumor activity in preclinical models in female mice. The 9-mer KRAS-G12V mutant peptide exhibits a distinct conformation from the 9-mer wildtype peptide and its 10-mer counterparts. Specific recognition of the G12V mutant by TCR depends both on distinct conformation from wildtype peptide and on direct interaction with residues from TCRs. Our study reveals the mechanisms of presentation and TCR recognition of KRAS-G12V mutant peptide and describes TCRs with therapeutic potency for tumor immunotherapy.

Benchmarking data-driven filtering for denoising of TCRpMHC single-cell data

Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality from high throughput studies of TCRpMHC-specificity by denoising is paramount in increasing our understanding of T cell-mediated immunity.

A robust deep learning workflow to predict CD8 + T-cell epitopes

BACKGROUND

T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.

METHODS

We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies.

RESULTS

TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP .

CONCLUSIONS

This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features

Introduction

T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity.

Methods

Identification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R.

Results

Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues.

Conclusion

Our results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.

Analysis of tumor infiltrating CD4+ and CD8+ CDR3 sequences reveals shared features putatively associated to the anti-tumor immune response

Introduction

Tumor-infiltrating lymphocytes (TILs) have predictive and prognostic value in breast cancer (BC) and exert a protective function against tumor growth, indicating that it is susceptible to treatment using adoptive cell transfer of TILs or T cell receptor (TCR)-based therapies. TCR can be used to identify naturally tumor-reactive T cells, but little is known about the differences in the TCR repertoires of CD4+ and CD8+ TILs.

Methods

TCR high-throughput sequencing was performed using TILs derived from the initial cultures of 11 BC biopsies and expanded and sorted CD4+ and CD8+ TILs as well as using PBMCs from healthy donors expanded and sorted using the same methodology.

Results

Physicochemical TCR differences between T cell subsets were observed, as CD4+ TILs presented larger N(D)Nnt TRB sequences and with a higher usage of positively charged residues, although only the latest was also observed in peripheral T cells from healthy individuals. Moreover, in CD4+ TILs, a more restricted TCR repertoire with a higher abundance of similar sequences containing certain amino acid motifs was observed.

Discussion

Some differences between CD4+ and CD8+ TCRs were intrinsic to T cell subsets as can also be observed in peripheral T cells from healthy individuals, while other were only found in TILs samples and therefore may be tumor-driven. Notably, the higher similarity among CD4+ TCRs suggests a higher TCR promiscuity in this subset.

Endotoxin contamination in commercially available Cas9 proteins potentially induces T-cell mediated responses

Immune responses to Cas proteins have been demonstrated recently and these may prove to be an impediment to their clinical use in gene editing. To make meaningful assessments of Cas9 immunogenicity during drug development and licensure it is imperative the reagents are free of impurities that could affect in vitro assessments of immunogenicity. Here we address the issue of endotoxin levels in laboratory grade Cas9 proteins used to measure T-cell memory responses. Many of these reagents have not been developed for immunogenicity assays, are or microbial origin and carry varying levels of endotoxin. The use of these reagents, off the shelf, without measuring endotoxin levels is likely to introduce incorrect estimates of the prevalence of memory T-cell responses in research studies. We demonstrate wide variation in endotoxin levels in Cas9 proteins from seven suppliers. Different lots from the same supplier also contained varying levels of endotoxin. ELISPOT assays showed similar large variations in the interferon-γ signals. Finally, when we carried out endotoxin depletion in four Cas9 proteins with strong signals in the ELISPOT assay, we found dampening of the interferon-γ signals.

BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

MOTIVATION

The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.

RESULTS

We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.

AVAILABILITY AND IMPLEMENTATION

The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.

Construction and Applications of Mammalian Cell-Based DNA-Encoded Peptide/Protein Libraries

DNA-encoded peptide/protein libraries are the starting point for protein evolutionary modification and functional peptide/antibody selection. Different display technologies, protein directed evolution, and deep mutational scanning (DMS) experiments employ DNA-encoded libraries to provide sequence variations for downstream affinity- or function-based selections. Mammalian cells promise the inherent post-translational modification and near-to-natural conformation of exogenously expressed mammalian proteins and thus are the best platform for studying transmembrane proteins or human disease-related proteins. However, due to the current technical bottlenecks of constructing mammalian cell-based large size DNA-encoded libraries, the advantages of mammalian cells as screening platforms have not been fully exploited. In this review, we summarize the current efforts in constructing DNA-encoded libraries in mammalian cells and the existing applications of these libraries in different fields.

High-dimensional single-cell analysis unveils distinct immune signatures of peripheral blood in patients with pancreatic ductal adenocarcinoma

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with poor response to immune checkpoint inhibitors. The mechanism of such poor response is not completely understood.

Methods

We assessed T-cell receptor (TCR) repertoire and RNA expression at the single-cell level using high-dimensional sequencing of peripheral blood immune cells isolated from PDAC patients and from healthy human controls. We validated RNA-sequencing data by performing mass cytometry (CyTOF) and by measuring serum levels of multiple immune checkpoint proteins.

Results

We found that proportions of T cells (CD45+CD3+) were decreased in PDAC patients compared to healthy controls, while proportion of myeloid cells was increased. The proportion of cytotoxic CD8+ T cells and the level of cytotoxicity per cell were increased in PDAC patients, with reduced TCR clonal diversity. We also found a significantly enriched S100A9+ monocyte population and an increased level of TIM-3 expression in immune cells of peripheral blood in PDAC patients. In addition, the serum level of soluble TIM-3 (sTIM-3) was significantly higher in PDAC patients compared to the non-PDAC participants and correlated with worse survival in two independent PDAC cohorts. Moreover, sTIM-3 exhibited a valuable role in diagnosis of PDAC, with sensitivity and specificity of about 80% in the training and validation groups, respectively. We further established an integrated model by combining sTIM-3 and carbohydrate antigen 19- 9 (CA19-9), which had an area under the curve of 0.974 and 0.992 in training and validation cohorts, respectively.

Conclusion

Our RNA-seq and proteomic results provide valuable insight for understanding the immune cell composition of peripheral blood of patients with PDAC.

Cell activation-based screening of natively paired human T cell receptor repertoires

Adoptive immune therapies based on the transfer of antigen-specific T cells have been used successfully to treat various cancers and viral infections, but improved techniques are needed to identify optimally protective human T cell receptors (TCRs). Here we present a high-throughput approach to the identification of natively paired human TCRα and TCRβ (TCRα:β) genes encoding heterodimeric TCRs that recognize specific peptide antigens bound to major histocompatibility complex molecules (pMHCs). We first captured and cloned TCRα:β genes from individual cells, ensuring fidelity using a suppression PCR. We then screened TCRα:β libraries expressed in an immortalized cell line using peptide-pulsed antigen-presenting cells and sequenced activated clones to identify the cognate TCRs. Our results validated an experimental pipeline that allows large-scale repertoire datasets to be annotated with functional specificity information, facilitating the discovery of therapeutically relevant TCRs.

Autoimmune susceptible HLA class II motifs facilitate the presentation of modified neoepitopes to potentially autoreactive T cells

Rheumatoid arthritis (RA), multiple sclerosis (MS), type 1 diabetes (T1D), and celiac disease (CD), are strongly associated with susceptible HLA class II haplotypes. The peptide-binding pockets of these molecules are polymorphic, thus each HLA class II protein presents a distinct set of peptides to CD4⁺ T cells. Peptide diversity is increased through post-translational modifications, generating non-templated sequences that enhance HLA binding and/or T cell recognition. The high-risk HLA-DR alleles that confer susceptibility to RA are notable for their ability to accommodate citrulline, promoting responses to citrullinated self-antigens. Likewise, HLA-DQ alleles associated with T1D and CD favor the binding of deamidated peptides. In this review, we discuss structural features that promote modified self-epitope presentation, provide evidence supporting the relevance of T cell recognition of such antigens in disease processes, and make a case that interrupting the pathways that generate such epitopes and reprogramming neoepitope-specific T cells are key strategies for effective therapeutic intervention.

SARS-CoV-2 epitope-specific T cells: Immunity response feature, TCR repertoire characteristics and cross-reactivity

The devastating COVID-19 pandemic caused by SARS-CoV-2 and multiple variants or subvariants remains an ongoing global challenge. SARS-CoV-2-specific T cell responses play a critical role in early virus clearance, disease severity control, limiting the viral transmission and underpinning COVID-19 vaccine efficacy. Studies estimated broad and robust T cell responses in each individual recognized at least 30 to 40 SARS-CoV-2 antigen epitopes and associated with COVID-19 clinical outcome. Several key immunodominant viral proteome epitopes, including S protein- and non-S protein-derived epitopes, may primarily induce potent and long-lasting antiviral protective effects. In this review, we summarized the immune response features of immunodominant epitope-specific T cells targeting different SRAS-CoV-2 proteome structures after infection and vaccination, including abundance, magnitude, frequency, phenotypic features and response kinetics. Further, we analyzed the epitopes immunodominance hierarchy in combination with multiple epitope-specific T cell attributes and TCR repertoires characteristics, and discussed the significant implications of cross-reactive T cells toward HCoVs, SRAS-CoV-2 and variants of concern, especially Omicron. This review may be essential for mapping the landscape of T cell responses toward SARS-CoV-2 and optimizing the current vaccine strategy.

Emerging phagocytosis checkpoints in cancer immunotherapy

Cancer immunotherapy, mainly including immune checkpoints-targeted therapy and the adoptive transfer of engineered immune cells, has revolutionized the oncology landscape as it utilizes patients' own immune systems in combating the cancer cells. Cancer cells escape immune surveillance by hijacking the corresponding inhibitory pathways via overexpressing checkpoint genes. Phagocytosis checkpoints, such as CD47, CD24, MHC-I, PD-L1, STC-1 and GD2, have emerged as essential checkpoints for cancer immunotherapy by functioning as "don't eat me" signals or interacting with "eat me" signals to suppress immune responses. Phagocytosis checkpoints link innate immunity and adaptive immunity in cancer immunotherapy. Genetic ablation of these phagocytosis checkpoints, as well as blockade of their signaling pathways, robustly augments phagocytosis and reduces tumor size. Among all phagocytosis checkpoints, CD47 is the most thoroughly studied and has emerged as a rising star among targets for cancer treatment. CD47-targeting antibodies and inhibitors have been investigated in various preclinical and clinical trials. However, anemia and thrombocytopenia appear to be formidable challenges since CD47 is ubiquitously expressed on erythrocytes. Here, we review the reported phagocytosis checkpoints by discussing their mechanisms and functions in cancer immunotherapy, highlight clinical progress in targeting these checkpoints and discuss challenges and potential solutions to smooth the way for combination immunotherapeutic strategies that involve both innate and adaptive immune responses.

The superantigens SpeC and TSST-1 specifically activate TRBV12-3/12-4+ memory T cells

Severe bacterial or viral infections can induce a state of immune hyperactivation that can culminate in a potentially lethal cytokine storm. The classic example is toxic shock syndrome, a life-threatening complication of Staphylococcus aureus or Streptococcus pyogenes infection, which is driven by potent toxins known as superantigens (SAgs). SAgs are thought to promote immune evasion via the promiscuous activation of T cells, which subsequently become hyporesponsive, and act by cross-linking major histocompatibility complex class II molecules on antigen-presenting cells to particular β-chain variable (TRBV) regions of αβ T cell receptors (TCRs). Although some of these interactions have been defined previously, our knowledge of SAg-responsive TRBV regions is incomplete. In this study, we found that CD4⁺ and CD8⁺ T cells expressing TRBV12-3/12-4⁺ TCRs were highly responsive to streptococcal pyrogenic exotoxin C (SpeC) and toxic shock syndrome toxin-1 (TSST-1). In particular, SpeC and TSST-1 specifically induced effector cytokine production and the upregulation of multiple coinhibitory receptors among TRBV12-3/12-4⁺ CD4⁺ and CD8⁺ memory T cells, and importantly, these biological responses were dependent on human leukocyte antigen (HLA)-DR. Collectively, these data provided evidence of functionally determinative and therapeutically relevant interactions between SpeC and TSST-1 and CD4⁺ and CD8⁺ memory T cells expressing TRBV12-3/12-4⁺ TCRs, mediated via HLA-DR.

The Monocytic Cell Line THP-1 as a Validated and Robust Surrogate Model for Human Dendritic Cells

We have implemented an improved, cost-effective, and highly reproducible protocol for a simple and rapid differentiation of the human leukemia monocytic cell line THP-1 into surrogates for immature dendritic cells (iDCs) or mature dendritic cells (mDCs). The successful differentiation of THP-1 cells into iDCs was determined by high numbers of cells expressing the DC activation markers CD54 (88%) and CD86 (61%), and the absence of the maturation marker CD83. The THP-1-derived mDCs are characterized by high numbers of cells expressing CD54 (99%), CD86 (73%), and the phagocytosis marker CD11b (49%) and, in contrast to THP-1-derived iDCs, CD83 (35%) and the migration marker CXCR4 (70%). Treatment of iDCs with sensitizers, such as NiSO4 and DNCB, led to high expression of CD54 (97%/98%; GMFI, 3.0/3.2-fold induction) and CD86 (64%/96%; GMFI, 4.3/3.2-fold induction) compared to undifferentiated sensitizer-treated THP-1 (CD54, 98%/98%; CD86, 55%/96%). Thus, our iDCs are highly suitable for toxicological studies identifying potential sensitizing or inflammatory compounds. Furthermore, the expression of CD11b, CD83, and CXCR4 on our iDC and mDC surrogates could allow studies investigating the molecular mechanisms of dendritic cell maturation, phagocytosis, migration, and their use as therapeutic targets in various disorders, such as sensitization, inflammation, and cancer.

Drastic transformation of visceral adipose tissue and peripheral CD4 T cells in obesity

Obesity has a pronounced effect on the immune response in systemic organs that results in not only insulin resistance but also altered immune responses to infectious diseases and malignant tumors. Obesity-associated microenvironmental changes alter transcriptional expression and metabolism in T cells, leading to alterations in T-cell differentiation, proliferation, function, and survival. Adipokines, cytokines, and lipids derived from obese visceral adipose tissue (VAT) may also contribute to the systemic T-cell phenotype, resulting in obesity-specific pathogenesis. VAT T cells, which have multiple roles in regulating homeostasis and energy utilization and defending against pathogens, are most susceptible to obesity. In particular, many studies have shown that CD4 T cells are deeply involved in the homeostasis of VAT endocrine and metabolic functions and in obesity-related chronic inflammation. In obesity, macrophages and adipocytes in VAT function as antigen-presenting cells and contribute to the obesity-specific CD4 T-cell response by inducing CD4 T-cell proliferation and differentiation into inflammatory effectors via interactions between major histocompatibility complex class II and T-cell receptors. When obesity persists, prolonged stimulation by leptin and circulating free fatty acids, repetitive antigen stimulation, activating stress responses, and hypoxia induce exhaustion of CD4 T cells in VAT. T-cell exhaustion is characterized by restricted effector function, persistent expression of inhibitory receptors, and a transcriptional state distinct from functional effector and memory T cells. Moreover, obesity causes thymic regression, which may result in homeostatic proliferation of obesity-specific T-cell subsets due to changes in T-cell metabolism and gene expression in VAT. In addition to causing T-cell exhaustion, obesity also accelerates cellular senescence of CD4 T cells. Senescent CD4 T cells secrete osteopontin, which causes further VAT inflammation. The obesity-associated transformation of CD4 T cells remains a negative legacy even after weight loss, causing treatment resistance of obesity-related conditions. This review discusses the marked transformation of CD4 T cells in VAT and systemic organs as a consequence of obesity-related microenvironmental changes.

Hybrid gMLP model for interaction prediction of MHC-peptide and TCR

Understanding the interaction of T-cell receptor (TCR) with major histocompatibility-peptide (MHC-peptide) complex is extremely important in human immunotherapy and vaccine development. However, due to the limited available data, the performance of existing models for predicting the interaction of T-cell receptors (TCR) with major histocompatibility-peptide complexes is still unsatisfactory. Deep learning models have been applied to prediction tasks in various fields and have achieved better results compared with other traditional models. In this study, we leverage the gMLP model combined with attention mechanism to predict the interaction of MHC-peptide and TCR. Experiments show that our model can predict TCR-peptide interactions accurately and can handle the problems caused by different TCR lengths. Moreover, we demonstrate that the models trained with paired CDR3β-chain and CDR3α-chain data are better than those trained with only CDR3β-chain or with CDR3α-chain data. We also demonstrate that the hybrid model has greater potential than the traditional convolutional neural network.

Attentive Variational Information Bottleneck for TCR-peptide interaction prediction

MOTIVATION

We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides.

RESULTS

Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences.

AVAILABILITY AND IMPLEMENTATION

The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Human leukocyte antigen super-locus: nexus of genomic supergenes, SNPs, indels, transcripts, and haplotypes

The human Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) super-locus is a highly polymorphic genomic region that encodes more than 140 coding genes including the transplantation and immune regulatory molecules. It receives special attention for genetic investigation because of its important role in the regulation of innate and adaptive immune responses and its strong association with numerous infectious and/or autoimmune diseases. In recent years, MHC genotyping and haplotyping using Sanger sequencing and next-generation sequencing (NGS) methods have produced many hundreds of genomic sequences of the HLA super-locus for comparative studies of the genetic architecture and diversity between the same and different haplotypes. In this special issue on 'The Current Landscape of HLA Genomics and Genetics', we provide a short review of some of the recent analytical developments used to investigate the SNP polymorphisms, structural variants (indels), transcription and haplotypes of the HLA super-locus. This review highlights the importance of using reference cell-lines, population studies, and NGS methods to improve and update our understanding of the mechanisms, architectural structures and combinations of human MHC genomic alleles (SNPs and indels) that better define and characterise haplotypes and their association with various phenotypes and diseases.

HLA variants have different preferences to present proteins with specific molecular functions which are complemented in frequent haplotypes

Human leukocyte antigen (HLA) genes are the most polymorphic loci in the human genome and code for proteins that play a key role in guiding adaptive immune responses by presenting foreign and self peptides (ligands) to T cells. Each person carries up to 6 HLA class I variants (maternal and paternal copies of HLA-A, HLA-B and HLA-C genes) and also multiple HLA class II variants, which cumulatively define the landscape of peptides presented to T cells. Each HLA variant has its own repertoire of presented peptides with a certain sequence motif which is mainly defined by peptide anchor residues (typically the second and the last positions for HLA class I ligands) forming key interactions with the peptide-binding groove of HLA. In this study, we aimed to characterize HLA binding preferences in terms of molecular functions of presented proteins. To focus on the ligand presentation bias introduced specifically by HLA-peptide interaction we performed large-scale in silico predictions of binding of all peptides from human proteome for a wide range of HLA variants and established which functions are characteristic for proteins that are more or less preferentially presented by different HLA variants using statistical calculations and gene ontology (GO) analysis. We demonstrated marked distinctions between HLA variants in molecular functions of preferentially presented proteins (e.g. some HLA variants preferentially present membrane and receptor proteins, while others - ribosomal and DNA-binding proteins) and reduced presentation of extracellular matrix and collagen proteins by the majority of HLA variants. To explain these observations we demonstrated that HLA preferentially presents proteins enriched in amino acids which are required as anchor residues for the particular HLA variant. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Taking into consideration that HLA alleles are inherited in haplotypes, we hypothesized that haplotypes composed of a combination of HLA variants with different presentation preferences should be more advantageous as they allow presenting a larger repertoire of peptides and avoiding holes in immunopeptidome. Indeed, we demonstrated that HLA-A/HLA-B and HLA-A/HLA-C haplotypes which have a high frequency in the human population are comprised of HLA variants that are more distinct in terms of functions of preferentially presented proteins than the control pairs.

T cell repertoire profiling in allografts and native tissues in recipients with COVID-19 after solid organ transplantation: Insight into T cell-mediated allograft protection from viral infection

Introduction

The effects of the SARS-CoV-2 virus on the body, and why the effects are more severe in certain patients, remain incompletely understood. One population of special interest is transplant recipients because of their immunosuppressed state. Understanding the pathophysiology of graft dysfunction in transplant patients with the COVID-19 viral syndrome is important for prognosticating the risk to the graft as well as understanding how best to prevent and, if necessary, treat graft injury in these patients.

Methods

We analyzed multiple types of solid organ transplant recipients (liver, kidney, heart or lung) at our institution who died from SARS-CoV-2 and underwent autopsy (n = 6) or whose grafts were biopsied during active SARS-CoV-2 infection (n = 8). Their serum inflammatory markers were examined together with the histological appearance, viral load, and TCR repertoire of their graft tissue and, for autopsy patients, several native tissues.

Results

Histology and clinical lab results revealed a systemic inflammatory pattern that included elevated inflammatory markers and diffuse tissue damage regardless of graft rejection. Virus was detected throughout all tissues, although most abundant in lungs. The TCR repertoire was broadly similar throughout the tissues of each individual, with greater sharing of dominant clones associated with more rapid disease course. There was no difference in viral load or clonal distribution of overall, COVID-associated, or putative SARS-CoV-2-specific TCRs between allograft and native tissue. We further demonstrated that SARSCoV-2-specific TCR sequences in transplant patients lack a donor HLArestricted pattern, regardless of distribution in allograft or native tissues,suggesting that recognition of viral antigens on infiltrating recipient cells can effectively trigger host T cell anti-viral responses in both the host and graft.

Discussion

Our findings suggest a systemic immune response to the SARS-CoV-2 virus in solid organ transplant patients that is not associated with rejection and consistent with a largely destructive effect of recipient HLA-restricted T cell clones that affects donor and native organs similarly.

Antigen-specific immune reactions by expanded CD8+ T cell clones from HLA-B*27-positive patients with spondyloarthritis

Spondyloarthritis (SpA) is a chronic inflammatory disease that is tightly linked to HLA-B*27 but the pathophysiological basis of this link is still unknown. It is discussed whether either the instability of HLA-B*27 molecules triggers predominantly innate immune reactions or yet unknown antigenic peptides presented by HLA-B*27 induce adaptive autoimmune reactions by CD8⁺ T cells. To analyze the pathogenesis of SpA, we here investigated the T cell receptor (TCR) usage and whole transcriptomes of CD8⁺ single cells from synovial fluid of HLA-B*27-positive SpA patients and HLA-B*27-negative controls. In HLA-B*27-positive patients, we confirmed preferential expression of several TCR β-chain families, found even more restricted usage of particular TCR α-chains, assigned matching TCR αβ-chain pairs with homologous CDR3-sequences, and detected identical TCR-chains in different patients. Gene expression analyses by single cell mRNAseq revealed that genes specific for the tissue resident memory phenotype, exhaustion, and apoptosis were particularly highly expressed in expanded clonotypes from HLA-B*27-positive SpA patients. Together, several independent lines of evidence argue in favor of an (auto)antigenic peptide related pathogenesis.

Tumor Antigen Loaded Nanovaccine Induced NIR-Activated Inflammation for Enhanced Antigen Presentation During Immunotherapy of Tumors

Although anticancer vaccines have achieved certain effects in early clinical practice, T cell immunity as the most common responsive pattern of anticancer vaccines is still limited by unsatisfied tumor recognition and inhibition efficiency. As the critical step of T cell immunity, uptake and presentation of specific antigen by antigen-presenting cells (APC) can be activated by inflammation for enhancing the response of T cells to the antigen source. Here, a hybrid nanovaccine named PTh/MnO₂ @M activated with a near-infrared ray (NIR) is prepared by coating an autologous tumor cell membrane on the surface of a polythiophene/MnO₂ composite core. The photoelectrical material polythiophene can produce local microinflammation under NIR radiation and activate specific T cell antitumor immunity by promoting APC maturation and autologous tumor antigens presentation. Moreover, the synthesized nanovaccine PTh/MnO₂ @M is shown to induce a significant antitumor immune response, effectively inhibit the progression of melanoma in mice, and significantly prolong the survival time of mice in vivo. This strategy aims to enhance T-cell immune responses by promoting antigen presentation, leading to effective and specific cancer therapy.

A class-mismatched TCR bypasses MHC restriction via an unorthodox but fully functional binding geometry

MHC restriction, which describes the binding of TCRs from CD4⁺ T cells to class II MHC proteins and TCRs from CD8⁺ T cells to class I MHC proteins, is a hallmark of immunology. Seemingly rare TCRs that break this paradigm exist, but mechanistic insight into their behavior is lacking. TIL1383I is a prototypical class-mismatched TCR, cloned from a CD4⁺ T cell but recognizing the tyrosinase tumor antigen presented by the class I MHC HLA-A2 in a fully functional manner. Here we find that TIL1383I binds this class I target with a highly atypical geometry. Despite unorthodox binding, TCR signaling, antigen specificity, and the ability to use CD8 are maintained. Structurally, a key feature of TIL1383I is an exceptionally long CDR3β loop that mediates functions that are traditionally performed separately by hypervariable and germline loops in canonical TCR structures. Our findings thus expand the range of known TCR binding geometries compatible with normal function and specificity, provide insight into the determinants of MHC restriction, and may help guide TCR selection and engineering for immunotherapy.

Rapid identification of CMV-specific TCRs via reverse TCR cloning system based on bulk TCR repertoire data

Advances in next-generation sequencing (NGS) have improved the resolution of T-cell receptor (TCR) repertoire analysis, and recent single-cell sequencing has made it possible to obtain information about TCR pairs. In our previous study, cytomegalovirus (CMV) pp65-specific T-cell response restricted by a single human leukocyte antigen (HLA) class I allotype was observed in an individual. Therefore, to effectively clone an antigen-specific TCR from these T cells, we developed a TCR cloning system that does not require a single cell level. First, we established the improved Jurkat reporter cell line, which was TCRαβ double knock-out and expressed CD8αβ molecules. Furthermore, functional TCRs were directly obtained by reverse TCR cloning using unique CDR3-specific PCR primers after bulk TCR sequencing of activation marker-positive CD8 T cells by NGS. A total of 15 TCRα and 14 TCRβ strands were successfully amplified by PCR from cDNA of 4-1BB-positive CD8 T cells restricted by HLA-A*02:01, HLA-A*02:06, HLA-B*07:02, and HLA-B*40:06. The panels with combinations of TCRα and TCRβ genes were investigated using Jurkat reporter cell line and artificial antigen-presenting cells (APCs). In two TCR pairs restricted by HLA-A*02:01, one TCR pair by HLA-A*02:06, four TCR pairs by HLA-B*07:02, and one TCR pair by HLA-B*40:06, their specificity and affinity were confirmed. The TCR pair of A*02:01/1-1 showed alloreactivity to HLA-A*02:06. The one TCR pair showed a higher response to the naturally processed antigen than that of the peptide pool. This reverse TCR cloning system will not only provide functional information to TCR repertoire analysis by NGS but also help in the development of TCR-T therapy.

AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding

T-cell receptors (TCRs) are formed by random recombination of genomic precursor elements, some of which mediate the recognition of cancer-associated antigens. Due to the complicated process of T-cell immune response and limited biological empirical evidence, the practical strategy for identifying TCRs and their recognized peptides is the computational prediction from population and/or individual TCR repertoires. In recent years, several machine/deep learning-based approaches have been proposed for TCR-peptide binding prediction. However, the predictive performances of these methods can be further improved by overcoming several significant flaws in neural network design. The interrelationship between amino acids in TCRs is critical for TCR antigen recognition, which was not properly considered by the existing methods. They also did not pay more attention to the amino acids that play a significant role in antigen-binding specificity. Moreover, complex networks tended to increase the risk of overfitting and computational costs. In this study, we developed a dual-input deep learning framework, named AttnTAP, to improve the TCR-peptide binding prediction. It used the bi-directional long short-term memory model for robust feature extraction of TCR sequences, which considered the interrelationships between amino acids and their precursors and postcursors. We also introduced the attention mechanism to give amino acids different weights and pay more attention to the contributing ones. In addition, we used the multilayer perceptron model instead of complex networks to extract peptide features to reduce overfitting and computational costs. AttnTAP achieved high areas under the curves (AUCs) in TCR-peptide binding prediction on both balanced and unbalanced datasets (higher than 0.838 on McPAS-TCR and 0.908 on VDJdb). Furthermore, it had the highest average AUCs in TPP-I and TPP-II tasks compared with the other five popular models (TPP-I: 0.84 on McPAS-TCR and 0.894 on VDJdb; TPP-II: 0.837 on McPAS-TCR and 0.893 on VDJdb). In conclusion, AttnTAP is a reasonable and practical framework for predicting TCR-peptide binding, which can accelerate identifying neoantigens and activated T cells for immunotherapy to meet urgent clinical needs.

A Novel Tolerogenic Antibody Targeting Disulfide-Modified Autoantigen Effectively Prevents Type 1 Diabetes in NOD Mice

Increasing evidence suggested that the islet amyloid polypeptide (IAPP) is an essential autoantigen in the pathogenesis of type 1 diabetes (T1D) in humans and non-obese diabetic (NOD) mice. A unique disulfide containing IAPP-derived peptide KS20 is one of the highly diabetogenic peptides in NOD mice. The KS20-reactive T cells, including prototypic pathogenic BDC5.2.9, accumulate in the pancreas of prediabetic and diabetic mice and contribute to disease development. We generated a monoclonal antibody (LD96.24) that interacts with IA^g7-KS20 complexes with high affinity and specificity. LD96.24 recognized the IA^g7-KS20 disulfide loop and blocked the interaction between IA^g7-KS20 tetramers and cognate T cells but not other autoantigen-reactive T cells. The in vivo LD96.24 studies, at either early or late stages, drastically induced tolerance and delayed the onset of T1D disease in NOD mice by reducing the infiltration of not only IAPP-specific T cells but also chromogranin A and insulin-specific T cells in the pancreas, together with B cells and dendritic cells. LD96.24 can also significantly increase the ratio of Foxp3⁺ regulatory T cells with Interferon-gamma-secreting effector T cells. Our data suggested the important role of disulfide-modified peptides in the development of T1D. Targeting the complexes of Major histocompatibility complex (MHC)/disulfide modified antigens would influence the thiol redox balance and could be a novel immunotherapy for T1D.

Human Leucocyte Antigens as Prognostic Markers in Head and Neck Squamous Cell Carcinoma

BACKGROUND

The induction and regulation of immune responses depend on human leucocyte antigen (HLA) molecules that present peptides derived from mutated neoantigens or tumor-associated antigens to cytotoxic T cells. The natural variation of HLA molecules might differ between tumor patients and the normal population. Thus, there might be associations between the frequencies of HLA alleles and the survival of tumor patients.

METHODS

This issue was studied in a cohort of 84 patients with head and neck squamous cell carcinomas (HNSCCs) of different localizations. The cohort was followed up for more than 10 years. HLA-A/B/C CTS-PCR-SSP typing at 1 field level from blood samples was performed, and the results were correlated with survival.

RESULTS

HLA-A*02 was the most prevalent allele in our cohort and was present in 51.1% of patients. The HLA-A*25 and HLA-C*06 alleles exhibited a significantly higher frequency in cancer patients than in the normal population of 174 blood and kidney donors (p = 0.02 and p = 0.01, respectively, Fisher's exact test). For HLA-C*04, a negative impact on overall survival in univariate analysis (p = 0.045) and a negative, but statistically insignificant effect on survival toward poorer survival in multivariate analysis (HR: 1.82; 95% CI: 0.99-3.34, p = 0.053) were observed. In addition, HLA-A*02 was also beneficial for overall survival and progression-free survival in multivariate analysis (HR 0.54; 95% CI: 0.31-0.92; p = 0.023).

CONCLUSION

HLA-A*02 allele expression might not only predict better survival but might also indicate superior tumor antigen presentation and, thus, help to select patients who could benefit from T-cell-dependent immunotherapies.