How T cells 'see' antigen

On the nature of signal 1 delivered to lymphocytes: A critical response to some considerations put forward in support of the quantum model of T cell activation

The original Two Signal Model of lymphocyte activation stated that antigen-dependent lymphocyte cooperation is required for lymphocyte activation, whereas a single or a few antigen-specific lymphocytes can be inactivated by antigen. A virtue of this model is its ability to account for peripheral tolerance. Both the activation and inactivation of lymphocytes were envisaged to require the lymphocytes' antigen-specific receptors to interact with antigen, leading to signal 1. We consider here the proposition that the sensitivity to antigen concentration for the generation of signal 1, to support both differentiation processes, is the same. This situation optimizes the reliability of peripheral tolerance and minimizes the effects of lymphocyte inactivation in decreasing the diversity of the lymphocytes. We consider the broader implications of this Principle of Parsimonious Sensitivity in regulating the activity of lymphocytes.

TPepRet: a deep learning model for characterizing T cell receptors-antigen binding patterns

MOTIVATION

T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding patterns between TCRs and peptides is critical for advancing these clinical applications. While several computational tools have been developed, they neglect the directional semantics inherent in sequence data, which are essential for accurately characterizing TCR-peptide interactions.

RESULTS

To address this gap, we develop TPepRet, an innovative model that integrates subsequence mining with semantic integration capabilities. TPepRet combines the strengths of the Bidirectional Gated Recurrent Unit (BiGRU) network for capturing bidirectional sequence dependencies with the Large Language Model framework to analyze subsequences and global sequences comprehensively, which enables TPepRet to accurately decipher the semantic binding relationship between TCRs and peptides. We have evaluated TPepRet to a range of challenging scenarios, including performance benchmarking against other tools using diverse datasets, analysis of peptide binding preferences, characterization of T cells clonal expansion, identification of true binder in complex environments, assessment of key binding sites through alanine scanning, validation against expression rates from large-scale datasets, and ability to screen SARS-CoV-2 TCRs. The comprehensive results suggest that TPepRet outperforms existing tools. We believe TPepRet will become an effective tool for understanding TCR-peptide binding in clinical treatment.

AVAILABILITY AND IMPLEMENTATION

The source code can be obtained from https://github.com/CSUBioGroup/TPepRet.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Learning predictive signatures of HLA type from T-cell repertoires

T cells recognize a wide range of pathogens using surface receptors that interact directly with peptides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles.

Revolutionizing cancer treatment: the rise of personalized immunotherapies

Interest in biological therapy for cancer has surged due to its precise targeting of cancer cells and minimized impact on surrounding healthy tissues. This review discusses various biological cancer therapies, highlighting advanced alternatives over conventional chemotherapy alone. It explores DNA and RNA-based vaccines, T-cell modifications, adoptive cell transfer, CAR T cell therapy, angiogenesis inhibitors, and the combination of immunotherapy with chemotherapy, offering a holistic view of the potential in cancer treatment. Additionally, it discusses the role of nanotechnology in increasing the efficacy of cancer-targeting drugs, as well as cytokine and immunoconjugate therapies for bolstering immune system effectiveness against neoplastic cells. The potential of gene potential for precise targeting of cancer-linked genes and the application of oncolytic viruses against virus-associated cancers are also discussed. The review identifies significant advancements in the targeted treatment of cancer by biological methods. It acknowledges the challenges, including drug resistance and the need for high specificity in certain therapies, while also highlighting the effectiveness of cancer vaccines, modified T-cells, and oncolytic viruses. Biological therapies are a promising frontier in cancer treatment, offering the potential for more personalized and effective therapeutic strategies. Despite existing challenges, ongoing research and clinical trials are fundamental for overcoming current limitations and enhancing the efficacy of biological therapies in cancer care.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

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.

In situ cell-surface conformation of the TCR-CD3 signaling complex

The extracellular molecular organization of the individual CD3 subunits around the αβ T cell receptor (TCR) is critical for initiating T cell signaling. In this study, we incorporate photo-crosslinkers at specific sites within the TCRα, TCRβ, CD3δ, and CD3γ subunits. Through crosslinking and docking, we identify a CD3ε'-CD3γ-CD3ε-CD3δ arrangement situated around the αβTCR in situ within the cell surface environment. We demonstrate the importance of cholesterol in maintaining the stability of the complex and that the 'in situ' complex structure mirrors the structure from 'detergent-purified' complexes. In addition, mutations aimed at stabilizing extracellular TCR-CD3 interfaces lead to poor signaling, suggesting that subunit fluidity is indispensable for signaling. Finally, employing photo-crosslinking and CD3 tetramer assays, we show that the TCR-CD3 complex undergoes minimal subunit movements or reorientations upon interaction with activating antibodies and pMHC tetramers. This suggests an absence of 'inactive-active' conformational states in the TCR constant regions and the extracellular CD3 subunits, unlike the transmembrane regions of the complex. This study contributes a nuanced understanding of TCR signaling, which may inform the development of therapeutics for immune-related disorders.

Proteomic profile of the antibody diversity in circulating extracellular vesicles of lung adenocarcinoma

Immunoglobulin diversity encompasses B-cell receptor, T-cell receptor, and antibody diversity. Existing studies have focused more on the role of B-cell and T-cell receptor diversity in tumor immunity, while the role of antibody diversity is less understood. This study examined and compared the blood extracellular vesicles (EVs) of lung cancer patients and healthy individuals using proteomics and bioinformatics analyses. The results revealed that among the 270 identified proteins, those involved in defense mechanisms were the most abundant. Most of these were antibody subtypes, accounting for 50.00%. Similarly, of the 40 identified EVs differentially expressed proteins (DEPs), 29 were involved in defense mechanisms (72.50%), with a higher proportion being antibody subtypes (82.76%). Furthermore, 24 DEP antibody subtypes were implicated in 18 immune reaction-related signaling pathways. These findings suggest that human serum EVs contain a significant number of antibody subtypes, and the antibody subtypes from lung cancer serum EVs differ from those of healthy controls (HCs). The variations in antibody diversity may be closely associated with lung cancer tumor immunity.

TIRTL-seq: Deep, quantitative, and affordable paired TCR repertoire sequencing

ɑ/β T cells are key players in adaptive immunity. The specificity of T cells is determined by the sequences of the hypervariable T cell receptor (TCR) ɑ and β chains. Although bulk TCR sequencing offers a cost-effective approach for in-depth TCR repertoire profiling, it does not provide chain pairings, which are essential for determining T cell specificity. In contrast, single-cell TCR sequencing technologies produce paired chain data, but are limited in throughput to thousands of cells and are cost-prohibitive for cohort-scale studies. Here, we present TIRTL-seq (Throughput-Intensive Rapid TCR Library sequencing), a novel approach that generates ready-to-sequence TCR libraries from live cells in less than 7 hours. The protocol is optimized for use with non-contact liquid handlers in an automation-friendly 384-well plate format. Reaction volume miniaturization reduces library preparation costs to <$0.50 per well. The core principle of TIRTL-seq is the parallel generation of hundreds of libraries providing multiple biological replicates from a single sample that allows precise inference of both frequencies of individual clones and TCR chain pairings from well-occurrence patterns. We demonstrate scalability of our approach up to 1 million unique paired αβTCR clonotypes corresponding to over 30 million T cells per sample at a cost of less than $2000. For a sample of 10 million cells the cost is ~$200. We benchmarked TIRTL-seq against state-of-the-art 5'RACE bulk TCR-seq and 10x Genomics Chromium technologies on longitudinal samples. We show that TIRTL-seq is able to quantitatively identify expanding and contracting clonotypes between timepoints while providing accurate TCR chain pairings, including distinct temporal dynamics of SARS-CoV-2-specific and EBV-specific CD8+ T cell responses after infection. While clonal expansion was followed by sharp contraction for SARS-CoV-2 specific TCRs, EBV-specific TCRs remained stable once established. The sequences of both ɑ and β TCR chains are essential for determining T cell specificity. As the field moves towards greater applications in diagnostics and immunotherapy that rely on TCR specificity, we anticipate that our scalable paired TCR sequencing methodology will be instrumental for collecting large paired-chain datasets and ultimately extracting therapeutically relevant information from the TCR repertoire.

Navigating through novelties concerning mCRC treatment-the role of immunotherapy, chemotherapy, and targeted therapy in mCRC

Over the course of nearly six decades since the inception of initial trials involving 5-FU in the treatment of mCRC (metastatic colorectal cancer), our progressive comprehension of the pathophysiology, genetics, and surgical techniques related to mCRC has paved the way for the introduction of novel therapeutic modalities. These advancements not only have augmented the overall survival but have also positively impacted the quality of life (QoL) for affected individuals. Despite the remarkable progress made in the last two decades in the development of chemotherapy, immunotherapy, and target therapies, mCRC remains an incurable disease, with a 5-year survival rate of 14%. In this comprehensive review, our primary goal is to present an overview of mCRC treatment methods following the latest guidelines provided by the National Comprehensive Cancer Network (NCCN), the American Society of Clinical Oncology (ASCO), and the American Society of Colon and Rectal Surgeons (ASCRS). Emphasis has been placed on outlining treatment approaches encompassing chemotherapy, immunotherapy, targeted therapy, and surgery's role in managing mCRC. Furthermore, our review delves into prospective avenues for developing new therapies, offering a glimpse into the future of alternative pathways that hold potential for advancing the field.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

Harnessing exosomes in theranostic applications: advancements and insights in gastrointestinal cancer research

Exosomes are small extracellular vesicles (30-150 nm) that are formed by endocytosis containing complex RNA as well as protein structures and are vital in intercellular communication and can be used in gene therapy and drug delivery. According to the cell sources of origin and the environmental conditions they are exposed to, these nanovesicles are very heterogeneous and dynamic in terms of content (cargo), size and membrane composition. Exosomes are released under physiological and pathological conditions and influence the pathogenesis of cancers through various mechanisms, including angiogenesis, metastasis, immune dysregulation, drug resistance, and tumor growth/development. Gastrointestinal cancer is one of the deadliest types of cancer in humans and can involve organs e.g., the esophagus and stomach, or others such as the liver, pancreas, small intestine, and colon. Early diagnosis is very important in this field because the overall survival of patients is low due to diagnosis in late stages and recurrence. Also, various therapeutic strategies have failed and there is an unmet need for the new therapeutic agents. Exosomes can become promising candidates in gastrointestinal cancers as biomarkers and therapeutic agents due to their lower immunity and passing the main physiological barriers. In this work, we provide a general overview of exosomes, their biogenesis and biological functions. In addition, we discuss the potential of exosomes to serve as biomarkers, agents in cancer treatment, drug delivery systems, and effective vaccines in immunotherapy, with an emphasis on gastrointestinal cancers.

Learning predictive signatures of HLA type from T-cell repertoires

T cells recognize a wide range of pathogens using surface receptors that interact directly with pep-tides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles.

Design and engineering of bispecific antibodies: insights and practical considerations

Bispecific antibodies (bsAbs) have attracted significant attention due to their dual binding activity, which permits simultaneous targeting of antigens and synergistic binding effects beyond what can be obtained even with combinations of conventional monospecific antibodies. Despite the tremendous therapeutic potential, the design and construction of bsAbs are often hampered by practical issues arising from the increased structural complexity as compared to conventional monospecific antibodies. The issues are diverse in nature, spanning from decreased biophysical stability from fusion of exogenous antigen-binding domains to antibody chain mispairing leading to formation of antibody-related impurities that are very difficult to remove. The added complexity requires judicious design considerations as well as extensive molecular engineering to ensure formation of high quality bsAbs with the intended mode of action and favorable drug-like qualities. In this review, we highlight and summarize some of the key considerations in design of bsAbs as well as state-of-the-art engineering principles that can be applied in efficient construction of bsAbs with diverse molecular formats.

Large extracellular vesicles derived from human regulatory macrophages (L-EVMreg) attenuate CD3/CD28-induced T-cell activation in vitro

Macrophages belong to the innate immune system, and we have recently shown that in vitro differentiated human regulatory macrophages (Mreg) release large extracellular vesicles (L-EVMreg) with an average size of 7.5 μm which regulate wound healing and angiogenesis in vitro. The aim of this study was to investigate whether L-EVMreg also affect the CD3/CD28-mediated activation of T-cells. Mreg were differentiated using blood monocytes and L-EVMreg were isolated from culture supernatants by differential centrifugation. Activation of human T-cells was induced by CD3/CD28-coated beads in the absence or presence of Mreg or different concentrations of L-EVMreg. Inhibition of T-cell activation was quantified by flow cytometry and antibodies directed against the T-cell marker granzyme B. Phosphatidylserine (PS) exposure on the surface of Mreg and L-EVMreg was analyzed by fluorescence microscopy. Incubation of human lymphocytes with CD3/CD28 beads resulted in an increase of cell size, cell granularity, and number of granzyme B-positive cells (P < 0.05) which is indicative of T-cell activation. The presence of Mreg (0.5 × 106 Mreg/ml) led to a reduction of T-cell activation (number of granzyme B-positive cells; P < 0.001), and a similar but less pronounced effect was also observed when incubating activated T-cells with L-EVMreg (P < 0.05 for 3.2 × 106 L-EVMreg/ml). A differential analysis of the effects of Mreg and L-EVMreg on CD4+ and CD8+ T-cells showed an inhibition of CD4+ T-cells by Mreg (P < 0.01) and L-EVMreg (P < 0.05 for 1.6 × 106 L-EVMreg/ml; P < 0.01 for 3.2 × 106 L-EVMreg/ml). A moderate inhibition of CD8+ T-cells was observed by Mreg (P < 0.05) and by L-EVMreg (P < 0.01 for 1.6 × 106 L-EVMreg/ml and 3.2 × 106 L-EVMreg/ml). PS was restricted to confined regions of the Mreg surface, while L-EVMreg showed strong signals for PS in the exoplasmic leaflet. L-EVMreg attenuate CD3/CD28-mediated activation of CD4+ and CD8+ T-cells. L-EVMreg may have clinical relevance, particularly in the treatment of diseases associated with increased T-cell activity. KEY MESSAGES: Mreg release large extracellular vesicles (L-EVMreg) with an average size of 7.5 µm L-EVMreg exhibit phosphatidylserine positivity L-EVMreg suppress CD4+ and CD8+ T-cells L-EVMreg hold clinical potential in T-cell-related diseases.

Identification of TCR repertoire patterns linked with anti-cancer immunotherapy

The highly diverse T cell receptor (TCR) repertoire is a crucial component of the adaptive immune system that aids in the protection against a wide variety of pathogens. This TCR repertoire, comprising the collection of all TCRs in an individual, is a valuable source of information on both recent and ongoing T cell activation. Cancer cells, like pathogens, have the ability to trigger an adaptive immune response. However, because cancer cells use a variety of strategies to escape immune responses, this is often insufficient to completely eradicate them. As a result, immunotherapy is a promising treatment option for cancer patients. This treatment is expected to increase T cell activation and subsequently alter the TCR repertoire composition in these patients. Monitoring TCR repertoires before and after immunotherapy can therefore provide additional insight into T cell responses and might identify cancer-associated TCR sequences. Here we present a computational strategy to identify those changes in the TCR repertoire that occur after treatment with immunotherapy. Since this method allows the identification of TCR patterns that might be treatment-associated, it can help future research by revealing those patterns that are related with response. This TCR analysis workflow is illustrated using public data from three different cancer patients who received anti-PD-1 treatment.

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.

TPBTE: A model based on convolutional Transformer for predicting the binding of TCR to epitope

T cell receptors (TCRs) selectively bind to antigens to fight pathogens with specific immunity. Current tools focus on the nature of amino acids within sequences and take less into account the nature of amino acids far apart and the relationship between sequences, leading to significant differences in the results from different datasets. We propose TPBTE, a model based on convolutional Transformer for Predicting the Binding of TCR to Epitope. It takes epitope sequences and the complementary decision region 3 (CDR3) sequences of TCRβ chain as inputs. And it uses a convolutional attention mechanism to learn amino acid representations between different positions of the sequences based on learning local features of the sequences. At the same time, it uses cross attention to learn the interaction information between TCR sequences and epitope sequences. A comprehensive evaluation of the TCR-epitope data shows that the average area under the curve of TPBTE outperforms the baseline model, and demonstrate an intentional performance. In addition, TPBTE can give the probability of binding TCR to epitopes, which can be used as the first step of epitope screening, narrowing the scope of epitope search and reducing the time of epitope search.

Computational design of candidate multi-epitope vaccine against SARS-CoV-2 targeting structural (S and N) and non-structural (NSP3 and NSP12) proteins

The COVID-19 pandemic caused by SARS-CoV-2 virus has created a global damage and has exposed the vulnerable side of scientific research towards novel diseases. The intensity of the pandemic is huge, with mortality rates of more than 6 million people worldwide in a span of 2 years. Considering the gravity of the situation, scientists all across the world are continuously attempting to create successful therapeutic solutions to combat the virus. Various vaccination strategies are being devised to ensure effective immunization against SARS-CoV-2 infection. SARS-CoV-2 spreads very rapidly, and the infection rate is remarkably high than other respiratory tract viruses. The viral entry and recognition of the host cell is facilitated by S protein of the virus. N protein along with NSP3 is majorly responsible for viral genome assembly and NSP12 performs polymerase activity for RNA synthesis. In this study, we have designed a multi-epitope, chimeric vaccine considering the two structural (S and N protein) and two non-structural proteins (NSP3 and NSP12) of SARS-CoV-2 virus. The aim is to induce immune response by generating antibodies against these proteins to target the viral entry and viral replication in the host cell. In this study, computational tools were used, and the reliability of the vaccine was verified using molecular docking, molecular dynamics simulation and immune simulation studies in silico. These studies demonstrate that the vaccine designed shows steady interaction with Toll like receptors with good stability and will be effective in inducing a strong and specific immune response in the body.Communicated by Ramaswamy H. Sarma.

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.

NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions

T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.

Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity

MOTIVATION

It has been proven that only a small fraction of the neoantigens presented by major histocompatibility complex (MHC) class I molecules on the cell surface can elicit T cells. This restriction can be attributed to the binding specificity of T cell receptor (TCR) and peptide-MHC complex (pMHC). Computational prediction of T cells binding to neoantigens is a challenging and unresolved task.

RESULTS

In this paper, we proposed an attention-aware contrastive learning model, ATMTCR, to infer the TCR-pMHC binding specificity. For each TCR sequence, we used a transformer encoder to transform it to latent representation, and then masked a percentage of amino acids guided by attention weights to generate its contrastive view. Compared to fully-supervised baseline model, we verified that contrastive learning-based pretraining on large-scale TCR sequences significantly improved the prediction performance of downstream tasks. Interestingly, masking a percentage of amino acids with low attention weights yielded best performance compared to other masking strategies. Comparison experiments on two independent datasets demonstrated our method achieved better performance than other existing algorithms. Moreover, we identified important amino acids and their positional preference through attention weights, which indicated the potential interpretability of our proposed model.

Clinical Correlation of Function and TCR vβ Diversity of MAGE-C2-Specific CD8+ T Cell Response in Esophageal Cancer

Melanoma-associated Ag (MAGE)-C2, an immunogenic cancer germline (testis) Ag, is highly expressed by various tumor cells, thymic medullary epithelial cells, and germ cells. In this study, we aimed to explore the immunologic properties of MAGE-C2-specific CD8+ T cells and the relationship of its TCR β-chain V region (TCR vβ) subfamily distribution to prognosis of patients with esophageal cancer. PBMCs and tumor-infiltrating lymphocytes expanded by CD3/CD28 Dynabeads and MAGE-C2 peptides in vitro resulted in the induction of lysosome-associated membrane protein-1 (LAMP-1 or CD107a) on the cell surface and the production of IFN-γ by MAGE-C2-specific CD8+ T cells. We found differential TCR vβ subfamily distribution among flow-sorted CD107a+IFN-γ+ and CD107a-IFN-γ- CD8+ T cells. The proportion of CD107a+ and/or IFN-γ+ tetramer+ CD8+ T cells was lower in patients with lymph node metastasis, late tumor stage, and poorly differentiated state (p < 0.05). T-box transcription factor was positively correlated with CD107a and IFN-γ. Kaplan-Meier analysis showed that patients whose MAGE-C2-specific CD8+ T cells expressed high CD107a and/or IFN-γ had a longer survival time when compared with patients whose MAGE-C2-specific CD8+ T cells expressed low levels of CD107a and/or IFN-γ. Moreover, analysis of TCR vβ subfamily distribution revealed that a higher frequency of TCR vβ16 in MAGE-C2-specific CD8+ T cells was positively correlated with a better prognosis. These results suggest that the presence of functional MAGE-C2-specific CD8+ T cells had an independent prognostic impact on the survival of patients with esophageal cancer.

Contact map dependence of a T-cell receptor binding repertoire

The T-cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T-cell receptor (TCR) specific to non-self-antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T-cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.

Naive and memory T cells TCR-HLA-binding prediction

T cells recognize antigens through the interaction of their T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC) molecule. Following thymic-positive selection, TCRs in peripheral naive T cells are expected to bind MHC alleles of the host. Peripheral clonal selection is expected to further increase the frequency of antigen-specific TCRs that bind to the host MHC alleles. To check for a systematic preference for MHC-binding T cells in TCR repertoires, we developed Natural Language Processing-based methods to predict TCR-MHC binding independently of the peptide presented for Class I MHC alleles. We trained a classifier on published TCR-pMHC binding pairs and obtained a high area under curve (AUC) of over 0.90 on the test set. However, when applied to TCR repertoires, the accuracy of the classifier dropped. We thus developed a two-stage prediction model, based on large-scale naive and memory TCR repertoires, denoted TCR HLA-binding predictor (CLAIRE). Since each host carries multiple human leukocyte antigen (HLA) alleles, we first computed whether a TCR on a CD8 T cell binds an MHC from any of the host Class-I HLA alleles. We then performed an iteration, where we predict the binding with the most probable allele from the first round. We show that this classifier is more precise for memory than for naïve cells. Moreover, it can be transferred between datasets. Finally, we developed a CD4-CD8 T cell classifier to apply CLAIRE to unsorted bulk sequencing datasets and showed a high AUC of 0.96 and 0.90 on large datasets. CLAIRE is available through a GitHub at: https://github.com/louzounlab/CLAIRE, and as a server at: https://claire.math.biu.ac.il/Home.

A multi-epitope vaccine candidate developed from unique immunogenic epitopes against Cryptosporidium hominis by utilizing an immunoinformatics-driven approach

An immunoinformatics-based strategy is being investigated to identify prospective multi-subunit vaccine candidates against Cryptosporidium hominis (C. hominis). We used a systematic technique based on protein structure to create a competent multi-subunit vaccine candidate against C. hominis, with the likelihood of antigenicity, allergenicity, and transmembrane helices as the screening criteria. Using the suitable linkers, the best-screened epitopes such as B-cell epitopes (BCL), Helper T-lymphocytes (HTL), and cytotoxic T-lymphocytes (CTL) were linked together to intensify and develop the presentation and processing of the antigenic molecules. The greatest 3 D model of the component protein was created with the help of modeling software called Raptorax. The validation of the modeled protein was accomplished via the use of PROCHECK. Furthermore, using the ClusPro web server, the projected modeled structure was docked with known receptor TLR-4 to determine their interactions. A molecular dynamics (MD) simulation was used to investigate the stability of the multi-subunit vaccine bound with TLR-4 based on the docking score. Aside from that, the codon optimization and in silico expression demonstrate the possibility of high expression and simple purification of the vaccine product resulting from codon optimization. The overall findings indicated that the multi-subunit vaccine might be a viable vaccination candidate against C. hominis.Communicated by Ramaswamy H. Sarma.

Intratumoral T-cell repertoires in DNA mismatch repair-proficient and -deficient colon tumors containing high or low numbers of tumor-infiltrating lymphocytes

Colon tumors with deficient DNA mismatch repair (dMMR) are generally infiltrated by T cells more densely than tumors with proficient mismatch repair (pMMR). However, high numbers of tumor-infiltrating lymphocytes (TILs) are found in select pMMR tumors, and low numbers of TILs are seen in select dMMR tumors. In this study, we compared T-cell repertoires in 20 pMMR and 27 dMMR colon tumors with high and low TIL counts. We found that T cells in dMMR tumors are more clonal and their repertoire is less rich compared with T cells in pMMR tumors. In the dMMR group, T cells in TIL-high tumors were more clonal and their repertoire was less rich compared with T cells in TIL-low tumors, but in the pMMR group, T-cell diversity in TIL-high tumors was comparable to T-cell diversity in TIL-low tumors. These findings suggest that T cells clonally expand in dMMR tumors, possibly in response to MMR deficiency-induced tumor neoantigens.

Immortalization and functional screening of natively paired human T cell receptor repertoires

Functional analyses of the T cell receptor (TCR) landscape can reveal critical information about protection from disease and molecular responses to vaccines. However, it has proven difficult to combine advanced next-generation sequencing technologies with methods to decode the peptide-major histocompatibility complex (pMHC) specificity of individual TCRs. We developed a new high-throughput approach to enable repertoire-scale functional evaluations of natively paired TCRs. In particular, we leveraged the immortalized nature of physically linked TCRα:β amplicon libraries to analyze binding against multiple recombinant pMHCs on a repertoire scale, and to exemplify the utility of this approach, we also performed affinity-based functional mapping in conjunction with quantitative next-generation sequencing to track antigen-specific TCRs. These data successfully validated a new immortalization and screening platform to facilitate detailed molecular analyses of disease-relevant antigen interactions with human TCRs.

T Cell Receptor Repertoire Analysis Reveals Signatures of T Cell Responses to Human Mycobacterium tuberculosis

Characterization of T cell receptor (TCR) repertoires is essential for understanding the mechanisms of Mycobacterium tuberculosis (Mtb) infection involving T cell adaptive immunity. The characteristics of TCR sequences and distinctive signatures of T cell subsets in tuberculous patients are still unclear. By combining single-cell TCR sequencing (sc-TCR seq) with single-cell RNA sequencing (sc-RNA seq) and flow cytometry to characterize T cells in tuberculous pleural effusions (TPEs), we identified 41,718 CD3⁺ T cells in TPEs and paired blood samples, including 30,515 CD4⁺ T cells and 11,203 CD8⁺ T cells. Compared with controls, no differences in length and profile of length distribution were observed in complementarity determining region 3 (CDR3) in both CD4⁺ and CD8⁺ T cells in TPE. Altered hydrophobicity was demonstrated in CDR3 in CD8⁺ T cells and a significant imbalance in the TCR usage pattern of T cells with preferential expression of TRBV4-1 in TPE. A significant increase in clonality was observed in TCR repertoires in CD4⁺ T cells, but not in CD8⁺ T cells, although both enriched CD4⁺ and CD8⁺ T cells showed T_H1 and cytotoxic signatures. Furthermore, we identified a new subset of polyfunctional CD4⁺ T cells with CD1-restricted, T_H1, and cytotoxic characteristics, and this subset might provide protective immunity against Mtb.

Heterologous Immunity of Virus-Specific T Cells Leading to Alloreactivity: Possible Implications for Solid Organ Transplantation

Exposure of the adaptive immune system to a pathogen can result in the activation and expansion of T cells capable of recognizing not only the specific antigen but also different unrelated antigens, a process which is commonly referred to as heterologous immunity. While such cross-reactivity is favourable in amplifying protective immune responses to pathogens, induction of T cell-mediated heterologous immune responses to allo-antigens in the setting of solid organ transplantation can potentially lead to allograft rejection. In this review, we provide an overview of murine and human studies investigating the incidence and functional properties of virus-specific memory T cells cross-reacting with allo-antigens and discuss their potential relevance in the context of solid organ transplantation.

In-silico design of peptides for inhibition of HLA-A*03-KLIETYFSK complex as a new drug design for treatment of multiples sclerosis disease

Multiple sclerosis is recognized as a chronic inflammatory disease. Human leukocyte antigen (HLA) plays an important role in initiating adaptive immune responses. HLA class I is present in almost all nucleated cells and presents the cleaved endogenous peptide antigens to cytotoxic T cells. HLA-A*03 is one of the HLA class I alleles, which is reported as substantially related HLA to MS disease. In 2011, the structure of the HLA-A*03 in complex was identified with an immunodominant proteolipid protein (PLP) epitope (KLIETYFSK). This complex has been reported as an important autoantigen-presenting complex in MS pathogenesis. In this study, new peptides were designed to bind to this complex that may prevent specific pathogenic cytotoxic T cell binding to this autoantigen-presenting complex and CNS demyelination. Herein, 14 new helical peptides containing 19 amino acids were designed and their structures were predicted using the PEP-FOLD server. The binding of each designed peptide to the mentioned complex was then performed. A mutation approach was used by the BeAtMuSiC server to improve the binding affinity of the designed peptide. In each position, amino acid substitutions leading to an increase in the binding affinity of the peptide to the mentioned complex were determined. Finally, the resulting complexes were simulated for 40 ns using AMBER18 software. The results revealed that out of 14 designed peptides, "WRYWWKDWAKQFRQFYRWF" peptide exhibited the highest affinity for binding to the mentioned complex. This peptide can be considered as a potential drug to control multiple sclerosis disease in patients carrying the HLA-A*03 allele.

Low neoantigen expression and poor T-cell priming underlie early immune escape in colorectal cancer

Immune evasion is a hallmark of cancer, and therapies that restore immune surveillance have proven highly effective in cancers with high tumor mutation burden (TMB) (e.g., those with microsatellite instability (MSI)). Whether low TMB cancers, which are largely refractory to immunotherapy, harbor potentially immunogenic neoantigens remains unclear. Here, we show that tumors from all patients with microsatellite stable (MSS) colorectal cancer (CRC) express clonal predicted neoantigens despite low TMB. Unexpectedly, these neoantigens are broadly expressed at lower levels compared to those in MSI CRC. Using a versatile platform for modulating neoantigen expression in CRC organoids and transplantation into the distal colon of mice, we show that low expression precludes productive cross priming and drives immediate T cell dysfunction. Strikingly, experimental or therapeutic rescue of priming rendered T cells capable of controlling tumors with low neoantigen expression. These findings underscore a critical role of neoantigen expression level in immune evasion and therapy response.

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

Currently, no approved vaccine is available against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes severe respiratory disease. The spike glycoprotein is typically considered a suitable target for MERS-CoV vaccine candidates. A computational strategy can be used to design an antigenic vaccine against a pathogen. Therefore, we used immunoinformatics and computational approaches to design a multi-epitope vaccine that targets the spike glycoprotein of MERS-CoV. After using numerous immunoinformatics tools and applying several immune filters, a poly-epitope vaccine was constructed comprising cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon-gamma (IFN-γ)-inducing epitopes. In addition, various physicochemical, allergenic, and antigenic profiles were evaluated to confirm the immunogenicity and safety of the vaccine. Molecular interactions, binding affinities, and the thermodynamic stability of the vaccine were examined through molecular docking and dynamic simulation approaches, during which we identified a stable and strong interaction with Toll-like receptors (TLRs). In silico immune simulations were performed to assess the immune-response triggering capabilities of the vaccine. This computational analysis suggested that the proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat viral infections; however, experimental evaluations remain necessary to verify the exact safety and immunogenicity profile of this vaccine.

In silico designing of vaccine candidate against Clostridium difficile

Clostridium difficile is a spore-forming gram-positive bacterium, recognized as the primary cause of antibiotic-associated nosocomial diarrhoea. Clostridium difficile infection (CDI) has emerged as a major health-associated infection with increased incidence and hospitalization over the years with high mortality rates. Contamination and infection occur after ingestion of vegetative spores, which germinate in the gastro-intestinal tract. The surface layer protein and flagellar proteins are responsible for the bacterial colonization while the spore coat protein, is associated with spore colonization. Both these factors are the main concern of the recurrence of CDI in hospitalized patients. In this study, the CotE, SlpA and FliC proteins are chosen to form a multivalent, multi-epitopic, chimeric vaccine candidate using the immunoinformatics approach. The overall reliability of the candidate vaccine was validated in silico and the molecular dynamics simulation verified the stability of the vaccine designed. Docking studies showed stable vaccine interactions with Toll‐Like Receptors of innate immune cells and MHC receptors. In silico codon optimization of the vaccine and its insertion in the cloning vector indicates a competent expression of the modelled vaccine in E. coli expression system. An in silico immune simulation system evaluated the effectiveness of the candidate vaccine to trigger a protective immune response.

Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction

Introduction

Predicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding.

Methods

We have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction.

Results and Discussion

ERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Immunopathogenic CSF TCR repertoire signatures in virus-associated neurologic disease

In this study, we examined and characterized disease-specific TCR signatures in cerebrospinal fluid (CSF) of patients with HTLV-1–associated myelopathy/tropical spastic paraparesis (HAM/TSP). TCR β libraries using unique molecular identifier–based methodologies were sequenced in paired peripheral blood mononuclear cells (PBMCs) and CSF cells from HAM/TSP patients and normal healthy donors (NDs). The sequence analysis demonstrated that TCR β repertoires in CSF of HAM/TSP patients were highly expanded and contained both TCR clonotypes shared with PBMCs and uniquely enriched within the CSF. In addition, we analyzed TCR β repertoires of highly expanded and potentially immunopathologic HTLV-1 Tax11-19–specific CD8⁺ T cells from PBMCs of HLA-A*0201⁺ HAM/TSP and identified a conserved motif (PGLAG) in the CDR3 region. Importantly, TCR β clonotypes of expanded clones in HTLV-1 Tax11-19–specific CD8⁺ T cells were also expanded and enriched in the CSF of the same patient. These results suggest that exploring TCR repertoires of CSF and antigen-specific T cells may provide a TCR repertoire signature in virus-associated neurologic disorders.

Immuno-informatics analysis and expression of a novel multi-domain antigen as a vaccine candidate against glioblastoma

Glioblastoma multiform is the most common of primary malignant brain tumors in adults. Currently, surgical resection of the tumor mass, followed by adjuvant radiotherapy and chemotherapy are standard treatments for glioblastoma multiform but so far are not effective treatments. Thus, the development of a vaccine, as a safe and efficient strategy for prophylactic or therapeutic purposes against glioblastoma multiform is very necessary. The present study aimed to design the multi-domain vaccine for glioblastoma multiform. An in silico approach was used to select the most potent domains of proteins to induce the host's B- and T-cell immune response against glioblastoma multiform. IL-13Rα-2 (amino acid positions 27-144), TNC (amino acid positions 1900-2100), and PTPRZ-1(amino acid positions 731-884) were found to have potent inducible immune responses. So, we considered them for fusing with a linker A(EAAAK)₃A to construct the multi-domain recombinant vaccine. The immuno-informatics analysis of the designed recombinant vaccine construct was performed to evaluate its efficacy. Although the designed recombinant vaccine construct did not show allergen property, its antigenicity was estimated at 0.78. The Physico-chemical properties of the recombinant vaccine construct were characterized and revealed the potency of the vaccine candidate. Then its secondary and tertiary structures, mRNA structure, molecular docking, and immune simulation were predicted using bioinformatics tools. Next, the designed recombinant vaccine construct was synthesized, and cloned into the pET28a vector and expressed in E. coli BL21. Besides, the circular dichroism spectroscopy was utilized for the investigation of the secondary structure changes of the recombinant vaccine construct. The results of the verification assessment of the recombinant vaccine construct expression indicated that in silico analysis was relatively accurate, and relatively change occurred on the protein secondary structure. In our future plan, the vaccine candidate that was confirmed by in silico tools should be validated by further in vitro and in vivo experimental studies.

Adoptive T cell therapy: Boosting the immune system to fight cancer

Cellular therapies have shown increasing promise as a cancer treatment. Encouraging results against hematologic malignancies are paving the way to move into solid tumors. In this review, we will focus on T-cell therapies starting from tumor infiltrating lymphocytes (TILs) to optimized T-cell receptor-modified (TCR) cells and chimeric antigen receptor-modified T cells (CAR-Ts). We will discuss the positive preclinical and clinical findings of these approaches, along with some of the persisting barriers that need to be overcome to improve outcomes.

Dexosomes as a cell-free vaccine for cancer immunotherapy

Dendritic cells (DCs) secrete vast quantities of exosomes termed as dexosomes. Dexosomes are symmetric nanoscale heat-stable vesicles that consist of a lipid bilayer displaying a characteristic series of lipid and protein molecules. They include tetraspanins and all established proteins for presenting antigenic material such as the major histocompatibility complex class I/II (MHC I/II) and CD1a, b, c, d proteins and CD86 costimulatory molecule. Dexosomes contribute to antigen-specific cellular immune responses by incorporating the MHC proteins with antigen molecules and transferring the antigen-MHC complexes and other associated molecules to naïve DCs. A variety of ex vivo and in vivo studies demonstrated that antigen-loaded dexosomes were able to initiate potent antitumor immunity. Human dexosomes can be easily prepared using monocyte-derived DCs isolated by leukapheresis of peripheral blood and treated ex vivo by cytokines and other factors. The feasibility of implementing dexosomes as therapeutic antitumor vaccines has been verified in two phase I and one phase II clinical trials in malignant melanoma and non small cell lung carcinoma patients. These studies proved the safety of dexosome administration and showed that dexosome vaccines have the capacity to trigger both the adaptive (T lymphocytes) and the innate (natural killer cells) immune cell recalls. In the current review, we will focus on the perspective of utilizing dexosome vaccines in the context of cancer immunotherapy.

Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.

Efficient Induction of Cytotoxic T Cells by Viral Vector Vaccination Requires STING-Dependent DC Functions

Modified Vaccinia virus Ankara (MVA) is an attenuated strain of vaccinia virus and currently under investigation as a promising vaccine vector against infectious diseases and cancer. MVA acquired mutations in host range and immunomodulatory genes, rendering the virus deficient for replication in most mammalian cells. MVA has a high safety profile and induces robust immune responses. However, the role of innate immune triggers for the induction of cytotoxic T cell responses after vaccination is incompletely understood. Stimulator of interferon genes (STING) is an adaptor protein which integrates signaling downstream of several DNA sensors and therefore mediates the induction of type I interferons and other cytokines or chemokines in response to various dsDNA viruses. Since the type I interferon response was entirely STING-dependent during MVA infection, we studied the effect of STING on primary and secondary cytotoxic T cell responses and memory T cell formation after MVA vaccination in STING KO mice. Moreover, we analyzed the impact of STING on the maturation of bone marrow-derived dendritic cells (BMDCs) and their functionality as antigen presenting cells for cytotoxic T cells during MVA infection in vitro. Our results show that STING has an impact on the antigen processing and presentation capacity of conventionel DCs and played a crucial role for DC maturation and type I interferon production. Importantly, STING was required for the induction of efficient cytotoxic T cell responses in vivo, since we observed significantly decreased short-lived effector and effector memory T cell responses after priming in STING KO mice. These findings indicate that STING probably integrates innate immune signaling downstream of different DNA sensors in DCs and shapes the cytotoxic T cell response via the DC maturation phenotype which strongly depends on type I interferons in this infection model. Understanding the detailed functions of innate immune triggers during MVA infection will contribute to the optimized design of MVA-based vaccines.

A candidate multi-epitope vaccine against SARS-CoV-2

In the past two decades, 7 coronaviruses have infected the human population, with two major outbreaks caused by SARS-CoV and MERS-CoV in the year 2002 and 2012, respectively. Currently, the entire world is facing a pandemic of another coronavirus, SARS-CoV-2, with a high fatality rate. The spike glycoprotein of SARS-CoV-2 mediates entry of virus into the host cell and is one of the most important antigenic determinants, making it a potential candidate for a vaccine. In this study, we have computationally designed a multi-epitope vaccine using spike glycoprotein of SARS-CoV-2. The overall quality of the candidate vaccine was validated in silico and Molecular Dynamics Simulation confirmed the stability of the designed vaccine. Docking studies revealed stable interactions of the vaccine with Toll-Like Receptors and MHC Receptors. The in silico cloning and codon optimization supported the proficient expression of the designed vaccine in E. coli expression system. The efficiency of the candidate vaccine to trigger an effective immune response was assessed by an in silico immune simulation. The computational analyses suggest that the designed multi-epitope vaccine is structurally stable which can induce specific immune responses and thus, can be a potential vaccine candidate against SARS-CoV-2.

Impact of T-cell receptor and B-cell receptor repertoire on the recurrence of early stage lung adenocarcinoma

Surgical resection is the only curative treatment for patients with early stage non-small cell lung cancer. However, approximately 33% of non-small cell lung cancer patients recur with the stage I disease, which may be attributed to a deficiency in antitumor immunity. In the present study, for early stage lung adenocarcinoma patients with early recurrence and early non-recurrence, we investigated the quantity of tumor-infiltrating T and B cells by immunohistochemistry, as well as the genes in the complementarity determining region 3 of the T-cell receptor β chain and the B-cell receptor immunoglobulin heavy chain. A decreased number of tumor-infiltrating lymphocytes cells (CD3⁺, CD4⁺, CD8⁺ and CD20⁺) was present in early recurrence patients. A significant increase in oligoclones and a reduction in T-cell receptor diversity were observed in the early recurrence group. Furthermore, there was a preference for V, J gene, and VJ gene combinations in patients with early recurrence versus non-recurrence, suggesting that this may be a new biomarker for the recurrence of early stage lung adenocarcinoma. These data indicate that T and B cell receptor repertoires influence the depth of human adaptive immune responses, and in addition to the quantity of tumor infiltrating T and B cells, may contribute to the prevention of early stage lung adenocarcinoma recurrence after surgical resection. Our study illustrates the potential value of the immune repertoire for predicting clinical efficacy and patient outcomes.