Individual T-cell Receptor Research Articles

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

α/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) α 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 α 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.

Targeting of multiple tumor-associated antigens by individual T cell receptors during successful cancer immunotherapy

The Tcells of the immune system can target tumors and clear solid cancers following tumor-infiltrating lymphocyte (TIL) therapy. We used combinatorial peptide libraries and a proteomic database to reveal the antigen specificities of persistent cancer-specific Tcell receptors (TCRs) following successful TIL therapy for stage IV malignant melanoma. Remarkably, individual TCRs could target multiple different tumor types via the HLA A∗02:01-restricted epitopes EAAGIGILTV, LLLGIGILVL, and NLSALGIFST from Melan A, BST2, and IMP2, respectively. Atomic structures of a TCR bound to all three antigens revealed the importance of the shared x-x-x-A/G-I/L-G-I-x-x-x recognition motif. Multi-epitope targeting allows individual Tcells to attack cancer in several ways simultaneously. Such "multipronged" Tcells exhibited superior recognition of cancer cells compared with conventional Tcell recognition of individual epitopes, making them attractive candidates for the development of future immunotherapies.

Assessing TCR identity, knock-in efficiency, and potency for individualized TCR-T cell therapy

Advances in mass spectrometry, genome sequencing techniques, and bioinformatic strategies have accelerated the discovery of cancer-specific neoantigens. Tumors express multiple immunogenic neoantigens, and neoantigen-specific T cell receptors (TCRs) can be identified in peripheral blood's mononuclear cells in cancer patients. Therefore, individualized TCR-based therapies are a promising approach whereby multiple neoantigen-specific TCRs can be selected in each patient, potentially leading to a highly effective treatment for cancer patients. We developed three multiplex analytical assays to determine the quality attributes of the TCR-T cell drug product with a mixture of five engineered TCRs. The identity of each TCR was determined by two NGS-based methods, Illumina MiSeq and PacBio platforms. This approach not only confirms the expected TCR sequences but also differentiates them by their variable regions. The five individual TCR and total TCR knock-in efficiencies were measured by droplet digital PCR using specific reverse primers. A potency assay based on transfection of antigen-encoding-RNA was developed to assess the dose-dependent activation of T cells for each TCR by measuring the surface activation marker CD137 expression and cytokine secretion. This work provides new assays to characterize individualized TCR-T cell products and insights into quality attributes for the control strategy.

Quantitative annotations of T-Cell repertoire specificity.

The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.

Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer.

Tumor behavior is intricately dependent on the oncogenic properties of cancer cells and their multi-cellular interactions. To understand these dependencies within the wider microenvironment, we studied over 270,000 single-cell transcriptomes and 100 microdissected whole exomes from 12 patients with kidney tumors, prior to validation using spatial transcriptomics. Tissues were sampled from multiple regions of the tumor core, the tumor-normal interface, normal surrounding tissues, and peripheral blood. We find that the tissue-type location of CD8+ Tcell clonotypes largely defines their exhaustion state with intra-tumoral spatial heterogeneity that is not well explained by somatic heterogeneity. De novo mutation calling from single-cell RNA-sequencing data allows us to broadly infer the clonality of stromal cells and lineage-trace myeloid cell development. We report six conserved meta-programs that distinguish tumor cell function, and find an epithelial-mesenchymal transition meta-program highly enriched at the tumor-normal interface that co-localizes with IL1B-expressing macrophages, offering a potential therapeutic target.

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.

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.

Application of T cell receptor (TCR) repertoire analysis for the advancement of cancer immunotherapy.

T cell receptor (TCR) sequencing has emerged as a powerful new technology in analysis of the host-tumour interaction. The advances in NextGen sequencing technologies, coupled with powerful novel bioinformatic tools, allow quantitative and reproducible characterisation of repertoires from tumour and blood samples from an increasing number of patients with a variety of solid cancers. In this review, we consider how global metrics such as T cell clonality and diversity can be extracted from these repertoires and used to give insight into the mechanism of action of immune checkpoint blockade. Furthermore, we explore how the analysis of TCR overlap between repertories can help define spatial and temporal heterogeneity of the anti-tumoural immune response. Finally, we review how analysis of TCR sequence and structure, either of individual TCRs or from sets of related TCRs can be used to annotate the antigenic specificity, with important implications for the development of personalised adoptive cellular immunotherapies.

A High-Throughput Strategy for T-Cell Receptor Cloning and Expression.

Expression of T-cell receptor (TCR) genes is a critical step for TCR characterization and epitope identification. The recent interest in using specific TCRs for cancer immunotherapy has further increased the demand for practical and robust methods to rapidly clone and express TCRs. We show that a recombination-based cloning protocol facilitates simple and rapid transfer of the TCR transgene into different expression systems. In this protocol, we first constructed all the human TRAV and TRBV genes into individual plasmid. To clone any TCR, we only need to ligate a short CDR3 fragment to its corresponding V gene plasmid using Golden Gate cloning. This strategy significantly improves the efficiency of individual TCR cloning and mutagenesis, providing a flexible high-throughput method for TCR analysis and TCR-mediated therapeutics.

HLA-A∗02:01 restricted Tcell receptors against the highly conserved SARS-CoV-2 polymerase cross-react with human coronaviruses.

Cross-reactivity and direct killing of target cells remain underexplored for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-specific CD8+ T cells. Isolation of T cell receptors (TCRs) and overexpression in allogeneic cells allows for extensive T cell reactivity profiling. We identify SARS-CoV-2 RNA-dependent RNA polymerase (RdRp/NSP12) as highly conserved, likely due to its critical role in the virus life cycle. We perform single-cell TCRαβ sequencing in human leukocyte antigen (HLA)-A∗02:01-restricted, RdRp-specific T cells from SARS-CoV-2-unexposed individuals. Human T cells expressing these TCRαβ constructs kill target cell lines engineered to express full-length RdRp. Three TCR constructs recognize homologous epitopes from common cold coronaviruses, indicating CD8+ T cells can recognize evolutionarily diverse coronaviruses. Analysis of individual TCR clones may help define vaccine epitopes that can induce long-term immunity against SARS-CoV-2 and other coronaviruses.

Non-Genetic Determinants of Clonotypic T Cell Expansion Following Allogeneic Stem Cell Transplant

Background:The immunologic basis of acute GVHD fundamentally involves alloreactive donor T cells that recognize foreign major histocompatibility complex (MHC)-peptide structures derived from both major and minor antigen mismatches with the host. Within this paradigm, the relationship between the donor and recipient genetics represents a closed system that dictates the potential ability of any given T cell receptor (TCR) to expand, raising the question of whether there are predictable aspects of TCR reconstitution at a clonal level. Take a hypothetical example - if genetically identical twins were to receive allogeneic grafts from the same donor and both recipients develop GVHD, would one expect similar TCRs to be clonally expanded? It has been challenging to rigorously explore this phenomenon, however, because of the vast combinatorial diversity of αβ TCRs, the high prevalence of low copy number TCRs, and sampling constraints - all of which render tracking and comparing TCR expansion between the donor and host difficult.Methods:We address these challenges in order to better understand the predictability TCR clonal dynamics through an analysis platform utilizing 1) a series of matched and mismatched murine transplant experiments where genetically identical littermates receive T cells from the same polyclonal donor pool, thus creating multiple transplant replicates simulating the twin transplant system describe above (Fig 1), and 2) probabilistic modeling of individual TCR frequencies to account for partitioning stochasticity (variation in how low copy number TCRs are distributed from donor to recipient). We conduct high-throughput DNA-based TCR amplicon sequencing for both donor and post-transplant recipient samples to generate over 20 million TCRs and model the expansion rates of all identifiable TCRs in each transplant system using a Bayesian approach.Results:While overall V and J gene usage were similar amongst identical recipients (Fig 2), we find that a small fraction of TCR clonotypes appears to have widely disparate clone counts amongst identical recipients receiving the same donor T cell pool. For example, we saw 9,739, 129 and 0 copies of a particular TCR in 3 different recipients in our B6->B6D2F1 system (Fig 3). In order to distinguish whether TCR count discrepancies seen across identical recipients is simply a reflection of donor partitioning stochasticity or true differential expansion (Fig 4), we apply a Bayesian algorithm to identify differential expanders, which represent TCRs that are asymmetrically expanded between recipients of a genetically identical pair (Fig 5). These TCRs can be generated from both memory and naïve T cell compartments.The presence of these differentially expanded clones amongst identical recipients suggests that non-genetic dependent mechanisms may influence which TCRs expand post-transplant. We next show that broad gut decontamination of microbiota with peri-transplant vancomycin, gentamicin, cefoxitin and metronidazole dramatically reduced the fraction of differential expanders (p<0.0001). However, the change in inflammation from microbiome depletion did not appear to drive this difference, as 1) MyD88/TRIF double knockout recipients (deficient TLR signaling) did not show a reduction in differential expanders, and 2) altering conditioning intensity (900cGy to 1300 cGy TBI) also did not change the fraction of differential expanders. Rather, the difference is likely antigenically driven, as differential expanders are enriched in antigen specificity compared to other TCR sequences (p<0.0001) based on published algorithm that identify TCRs with similar amino acid sequence overlap.Conclusions:These results refine our current understanding of clonal T cell selection and expansion after allogeneic BMT and suggests that for a given transplant system, individual TCR selection is not solely dictated by genetic donor and recipient major and/or minor histocompatibility disparities. Rather, microbiota-derived molecules appear to behave as minor antigens to direct systemic clonal TCR selection. These data suggest a novel mechanism by which the microbiome may modulate transplant outcome, challenging current paradigms suggesting the microbiota primarily drive inflammation via their PAMP activities. [Display omitted] DisclosuresHill: Applied Molecular Transport: Research Funding; Syndax Pharmaceuticals: Research Funding; Compass Therapeutics: Research Funding; NapaJun Pharma: Consultancy; Generon corporation: Consultancy; iTeos Therapeutics: Consultancy, Research Funding; Neoleukin Therapeutics: Consultancy; Roche: Research Funding.

T cells in harmony: Aligning the TCR repertoire pool to identify microbiota recognizing T cells

Microbiota-specific Tcell responses have been identified for select microbes, but individual Tcell receptor repertoire differences make characterizing responses across populations difficult. In this issue of Immunity,Muschaweck etal. establish a system allowing for reproducible responses between individual mice, a powerful tool for characterizing microbiota directed immunity.

PD-1 preferentially inhibits the activation of low-affinity T cells

Anti-PD-1 therapies can activate tumor-specific T cells to destroy tumors. However, whether and how T cells with different antigen specificity and affinity are differentially regulated by PD-1 remain vaguely understood. Upon antigen stimulation, a variety of genes is induced in T cells. Recently, we found that T cell receptor (TCR) signal strength required for the induction of genes varies across different genes and PD-1 preferentially inhibits the induction of genes that require stronger TCR signal. As each T cell has its own response characteristics, inducibility of genes likely differs across different T cells. Accordingly, the inhibitory effects of PD-1 are also expected to differ across different T cells. In the current study, we investigated whether and how factors that modulate T cell responsiveness to antigenic stimuli influence PD-1 function. By analyzing TCRs with different affinities to peptide-MHC complexes (pMHC) and pMHCs with different affinities to TCR, we demonstrated that PD-1 inhibits the expression of TCR-inducible genes efficiently when TCR:pMHC affinity is low. In contrast, affinities of peptides to MHC and MHC expression levels did not affect PD-1 sensitivity of TCR-inducible genes although they markedly altered the dose responsiveness of T cells by changing the efficiency of pMHC formation, suggesting that the strength of individual TCR signal is the key determinant of PD-1 sensitivity. Accordingly, we observed a preferential expansion of T cells with low-affinity to tumor-antigen in PD-1-deficient mice upon inoculation of tumor cells. These results demonstrate that PD-1 imposes qualitative control of T cell responses by preferentially suppressing low-affinity T cells.

Abstract 1736: Antigen-specific targeting of tissue-resident gamma delta T cells with recombinant butyrophilin heterodimeric fusion proteins

Abstract A major mechanism of acquired resistance to immune checkpoint inhibition involves downregulation of antigen presentation, including the MHC I complex itself. Downregulation of antigen presentation on MHC I can render tumor cells invisible to any α/β T cell directed therapy. γδ T cells (γδT) are a small subset of the overall T cell compartment, but are characterized by increased cytolytic capacity relative to α/β T cells. Rather than MHC I, γδT recognize target cells via a complex of heterodimerized butyrophilin (BTN) proteins. Thus, display of BTN heterodimers on the surface of tumor cells may enhance immunity to tumors that have downregulated MHC I, or which express low abundance or low affinity antigens. We have previously reported the generation of a heterodimeric BTN protein targeting the CD19 antigen, referred to as BTN2A1/3A1-Fc-CD19scFv. While the BTN2A1/3A1 complex is a potent activator of γδT expressing the Vγ9δ2 T cell receptor (TCR), which is the major γδT population in human peripheral blood, Vγ9δ2 T cells are not the predominant γδT in many tissues. The murine equivalent of BTN2A1/3A1-Fc-CD19scFv, BTNL1/6-Fc-CD19scFv, stimulated specific proliferation and increased the cytolytic capacity of peripheral blood γδT in mice, but not other tissue-restricted GDT populations. These data suggested that distinct BTN heterodimers may preferentially activate tissue-restricted subsets of GDT. To characterize potential differences between peripheral blood and tissue-restricted γδT , we performed a multi-layered analysis, including single-cell sequencing of γδ TCR from paired peripheral blood and tumor tissues from human cancer patients such as melanoma, prostate and colon cancer. These data identified tissue-specific preferences for individual γδ TCRs, with corresponding tissue-specific preferences for individual BTN proteins. Based on this information, we generated a panel of distinct heterodimeric BTN proteins, and show that specific γδT populations are preferentially activated by specific BTN heterodimers in a lock-and-key fashion. These data are a necessary pre-requisite for designing γδT specific therapeutics that may target both immune neglected and acquired resistant tumors that have limited visibility to α/β T cell directed approaches. Citation Format: Suresh de Silva, George Fromm, Anne Lai, Louis Gonzalez, Arpita Patel, Kyung Jin Yoo, Kellsey Johannes, Kinsley Evans, Keith Wilson, Taylor H. Schreiber. Antigen-specific targeting of tissue-resident gamma delta T cells with recombinant butyrophilin heterodimeric fusion proteins [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1736.

Probing the Conformational Dynamics of Affinity-Enhanced T Cell Receptor Variants upon Binding the Peptide-Bound Major Histocompatibility Complex by Hydrogen/Deuterium Exchange Mass Spectrometry.

Binding of the T cell receptor (TCR) to its cognate, peptide antigen-loaded major histocompatibility complex (pMHC) is a key interaction for triggering T cell activation and ultimately elimination of the target cell. Despite the importance of this interaction for cellular immunity, a comprehensive molecular understanding of TCR specificity and affinity is lacking. We conducted hydrogen/deuterium exchange mass spectrometry (HDX-MS) analyses of individual affinity-enhanced TCR variants and clinically relevant pMHC class I molecules (HLA-A*0201/NY-ESO-1157-165) to investigate the causality between increased binding affinity and conformational dynamics in TCR-pMHC complexes. Differential HDX-MS analyses of TCR variants revealed that mutations for affinity enhancement in TCR CDRs altered the conformational response of TCR to pMHC ligation. Improved pMHC binding affinity was in general observed to correlate with greater differences in HDX upon pMHC binding in modified TCR CDR loops, thereby providing new insights into the TCR-pMHC interaction. Furthermore, a specific point mutation in the β-CDR3 loop of the NY-ESO-1 TCR associated with a substantial increase in binding affinity resulted in a substantial change in pMHC binding kinetics (i.e., very slow kon, revealed by the detection of EX1 HDX kinetics), thus providing experimental evidence for a slow induced-fit binding mode. We also examined the conformational impact of pMHC binding on an unrelated TRAV12-2 gene-encoded TCR directed against the immunodominant MART-126-35 cancer antigen restricted by HLA-A*0201. Our findings provide a molecular basis for the observed TRAV12-2 gene bias in natural CD8+ T cell-based immune responses against the MART-1 antigen, with potential implications for general ligand discrimination and TCR cross-reactivity processes.

γδ T-cell Receptors Derived from Breast Cancer-Infiltrating T Lymphocytes Mediate Antitumor Reactivity.

γδ T cells in human solid tumors remain poorly defined. Here, we describe molecular and functional analyses of T-cell receptors (TCR) from tumor-infiltrating γδ T lymphocytes (γδ TIL) that were in direct contact with tumor cells in breast cancer lesions from archival material. We observed that the majority of γδ TILs harbored a proinflammatory phenotype and only a minority associated with the expression of IL17. We characterized TCRγ or TCRδ chains of γδ TILs and observed a higher proportion of Vδ2+ T cells compared with other tumor types. By reconstructing matched Vδ2- TCRγ and TCRδ pairs derived from single-cell sequencing, our data suggest that γδ TILs could be active against breast cancer and other tumor types. The reactivity pattern against tumor cells depended on both the TCRγ and TCRδ chains and was independent of additional costimulation through other innate immune receptors. We conclude that γδ TILs can mediate tumor reactivity through their individual γδ TCR pairs and that engineered T cells expressing TCRγ and δ chains derived from γδ TILs display potent antitumor reactivity against different cancer cell types and, thus, may be a valuable tool for engineering immune cells for adoptive cell therapies.

GPU-Accelerated Discovery of Pathogen-Derived Molecular Mimics of a T-Cell Insulin Epitope.

The strong links between (Human Leukocyte Antigen) HLA, infection and autoimmunity combine to implicate T-cells as primary triggers of autoimmune disease (AD). T-cell crossreactivity between microbially-derived peptides and self-peptides has been shown to break tolerance and trigger AD in experimental animal models. Detailed examination of the potential for T-cell crossreactivity to trigger human AD will require means of predicting which peptides might be recognised by autoimmune T-cell receptors (TCRs). Recent developments in high throughput sequencing and bioinformatics mean that it is now possible to link individual TCRs to specific pathologies for the first time. Deconvolution of TCR function requires knowledge of TCR specificity. Positional Scanning Combinatorial Peptide Libraries (PS-CPLs) can be used to predict HLA-restriction and define antigenic peptides derived from self and pathogen proteins. In silico search of the known terrestrial proteome with a prediction algorithm that ranks potential antigens in order of recognition likelihood requires complex, large-scale computations over several days that are infeasible on a personal computer. We decreased the time required for peptide searching to under 30 min using multiple blocks on graphics processing units (GPUs). This time-efficient, cost-effective hardware accelerator was used to screen bacterial and fungal human pathogens for peptide sequences predicted to activate a T-cell clone, InsB4, that was isolated from a patient with type 1 diabetes and recognised the insulin B-derived epitope HLVEALYLV in the context of disease-risk allele HLA A*0201. InsB4 was shown to kill HLA A*0201+ human insulin producing β-cells demonstrating that T-cells with this specificity might contribute to disease. The GPU-accelerated algorithm and multispecies pathogen proteomic databases were validated to discover pathogen-derived peptide sequences that acted as super-agonists for the InsB4 T-cell clone. Peptide-MHC tetramer binding and surface plasmon resonance were used to confirm that the InsB4 TCR bound to the highest-ranked peptide agonists derived from infectious bacteria and fungi. Adoption of GPU-accelerated prediction of T-cell agonists has the capacity to revolutionise our understanding of AD by identifying potential targets for autoimmune T-cells. This approach has further potential for dissecting T-cell responses to infectious disease and cancer.

P083 Characterisation of T-cell receptor repertoire patterns of circulating a4b7 positive memory T cells in ulcerative colitis

Abstract Background Memory T cells play an important role in mediating inflammatory responses in IBD. The integrin a4b7 is highly expressed on activated T cells, and is thought to direct homing of lymphocytes to the intestine, following its binding to MADCAM-1 expressed exclusively on intestinal endothelial cells. Since UC is characterised by oligoclonal expansion of specific T-cell clonotypes, we hypothesised that circulating memory T cells with gut-homing potential would exhibit unique T-cell receptor repertoire features. Methods Peripheral blood mononuclear cells were collected from 5 control subjects and 6 pediatric patients with active UC. Following CD3 MACS sorting cells were FACS sorted into a4b7 positive and a4b7 negative CD3+CD45RO+ memory T cells. DNA was Isolated from each subset and subjected to next-generation sequencing of the TCRB. This high-throughput platform employs massive parallel sequencing to process millions of rearranged T-cell receptor (TCR) products simultaneously, and permits an in-depth analysis of individual TCRs at the nucleotide level. Comparisons of different indices of diversity, CDR3 length and clonal biochemical characteristics were performed between a4b7 positive and a4b7 negative populations for each subject, and between controls and UC patients. Results PBMCs were isolated from active UC patients during endoscopic assessment. Four patients had a Mayo endoscopic score of 2, and two patients had a score of 1. Only one patient was treated with an immunosuppressive medication (azathioprine), and five out of six patients were treated with 5ASAs. Percentages of memory T cells (43.8 ± 12.3% vs. 32.2 ± 13.1%, p = 0.17) and a4b7 positive T cells (33.6 ± 15.7% vs. 36.0 ± 17.6%, p = 0.81) were comparable between controls and UC patients. Interestingly, a4b7 positive memory T cells displayed a polyclonal distribution, in both control subjects and in UC patients, without expansion of specific clones. Different indices of diversity, including shanon’s H, clonality index and entropy, were similar among controls and patients, both for a4b7 positive and a4b7 negative populations. Finally, clonal overlap between a4b7 positive and a4b7 negative memory T cells, for each subject was high, ranging between 30–50% for controls and 27–48% for UC patients. Conclusion a4b7 expressing memory T cells exhibited a polyclonal repertoire in both control subjects and patients with active UC, with high rates of overlap with a4b7 negative memory T cells. Our study, along with additional recent reports, challenge the dogma of the importance of a4b7 expression for T-cell migration to the gut, and may suggest that vedolizumab’s suppresses intestinal inflammation by blocking the trafficking of innate immune subsets.

Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires.

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

An overview of immunoinformatics approaches and databases linking T cell receptor repertoires to their antigen specificity.

Recent advances in molecular and bioinformatic methods have greatly improved our ability to study the formation of an adaptive immune response towards foreign pathogens, self-antigens, and cancer neoantigens. T cell receptors (TCR) are the key players in this process that recognize peptides presented by major histocompatibility complex (MHC). Owing to the huge diversity of both TCR sequence variants and peptides they recognize, accumulation and complex analysis of large amounts of TCR-antigen specificity data is required for understanding the structure and features of adaptive immune responses towards pathogens, vaccines, cancer, as well as autoimmune responses. In the present review, we summarize recent efforts on gathering and interpreting TCR-antigen specificity data and outline the critical role of tighter integration with other immunoinformatics data sources that include epitope MHC restriction, TCR repertoire structure models, and TCR/peptide/MHC structural data. We suggest that such integration can lead to the ability to accurately annotate individual TCR repertoires, efficiently estimate epitope and neoantigen immunogenicity, and ultimately, in silico identify TCRs specific to yet unstudied antigens and predict self-peptides related to autoimmunity.