CDR3α drives selection of the immunodominant Epstein Barr virus (EBV) BRLF1-specific CD8 T cell receptor repertoire in primary infection

Comparative analysis of the immune repertoire between peripheral blood and bone marrow fluids in those infected by EBV and immunodeficiency: A retrospective case study

High-throughput immune repertoire (IR) sequencing provides direct insight into the diversity of B cell receptor (BCR) and T cell receptor (TCR), with great potential to revolutionize the diagnosis, monitoring, and prevention of immune system-related disorders. In this study, multiplex PCR was applied to amplify the complementarity-determining regions of BCR and TCR, followed by comprehensive analysis by high-throughput sequencing. We compare the TCR (BCR) of bone marrow fluid (BMF) and peripheral blood (PB) samples from 17 patients in the Epstein-Barr and immunodeficiency groups, respectively. Our study shows that the diversity of the IR of blood samples is very similar to that of bone marrow samples statistically. However, the distributions of the monoclonal genes are significantly different in these 2 samples of most patients. This suggests that the BMFs can be replaced by the PB samples in diversity detection of IR to monitor the immune status of the body, while the detection of the BMFs is unreplaceable when the monoclonal change occurs. We used high-throughput sequencing to assess the TCR and BCR of the patients and provide a basis for the clinical analysis of PB and bone marrow samples and selection of disease diagnosis and monitoring methods.

CD4+ tumor-infiltrating lymphocytes secreting T cell-engagers induce regression of autologous patient-derived non-small cell lung cancer xenografts

Adoptive transfer of tumor-infiltrating lymphocytes (TIL) has shown remarkable results in melanoma, but only modest clinical benefits in other cancers, even after TIL have been genetically modified to improve their tumor homing, cytotoxic potential or overcome cell exhaustion. The required ex vivo TIL expansion process may induce changes in the T cell clonal composition, which could likely compromise the tumor reactivity of TIL preparations and ultimately the success of TIL therapy. A promising approach based on the production of bispecific T cell-engagers (TCE) by engineered T cells (STAb-T therapy) improves the efficacy of current T cell redirection strategies against tumor-associated antigens in hematological tumors. We studied the TCRβ repertoire in non-small cell lung cancer (NSCLC) tumors and in ex vivo expanded TIL from two unrelated patients. We generated TIL secreting anti-epidermal growth factor receptor (EGFR) × anti-CD3 TCE (TILSTAb) and tested their antitumor efficacy in vitro and in vivo using a NSCLC patient-derived xenograft (PDX) model in which tumor fragments and TIL from the same patient were transplanted into hIL-2 NOG mice. We confirmed that the standard TIL expansion protocol promotes the loss of tumor-dominant T cell clones and the overgrowth of virus-reactive TCR clonotypes that were marginally detectable in primary tumors. We demonstrated the antitumor activity of TILSTAb both in vitro and in vivo when administered intratumorally and systemically in an autologous immune-humanized PDX EGFR+ NSCLC mouse model, where tumor regression was mediated by TCE-redirected CD4+ TIL bearing non-tumor dominant clonotypes.

Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity

The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.

T Cell Receptor Chain Centricity: The Phenomenon and Potential Applications in Cancer Immunotherapy

T cells are crucial players in adaptive anti-cancer immunity. The gene modification of T cells with tumor antigen-specific T cell receptors (TCRs) was a milestone in personalized cancer immunotherapy. TCR is a heterodimer (either α/β or γ/δ) able to recognize a peptide antigen in a complex with self-MHC molecules. Although traditional concepts assume that an α- and β-chain contribute equally to antigen recognition, mounting data reveal that certain receptors possess chain centricity, i.e., one hemi-chain TCR dominates antigen recognition and dictates its specificity. Chain-centric TCRs are currently poorly understood in terms of their origin and the functional T cell subsets that express them. In addition, the ratio of α- and β-chain-centric TCRs, as well as the exact proportion of chain-centric TCRs in the native repertoire, is generally still unknown today. In this review, we provide a retrospective analysis of studies that evidence chain-centric TCRs, propose patterns of their generation, and discuss the potential applications of such receptors in T cell gene modification for adoptive cancer immunotherapy.

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

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules

Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide-HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.

Public T-Cell Receptors (TCRs) Revisited by Analysis of the Magnitude of Identical and Highly-Similar TCRs in Virus-Specific T-Cell Repertoires of Healthy Individuals

Since multiple different T-cell receptor (TCR) sequences can bind to the same peptide-MHC combination and the number of TCR-sequences that can theoretically be generated even exceeds the number of T cells in a human body, the likelihood that many public identical (PUB-I) TCR-sequences frequently contribute to immune responses has been estimated to be low. Here, we quantitatively analyzed the TCR-repertoires of 190 purified virus-specific memory T-cell populations, directed against 21 epitopes of Cytomegalovirus, Epstein-Barr virus and Adenovirus isolated from 29 healthy individuals, and determined the magnitude, defined as prevalence within the population and frequencies within individuals, of PUB-I TCR and of TCR-sequences that are highly-similar (PUB-HS) to these PUB-I TCR-sequences. We found that almost one third of all TCR nucleotide-sequences represented PUB-I TCR amino-acid (AA) sequences and found an additional 12% of PUB-HS TCRs differing by maximally 3 AAs. We illustrate that these PUB-I and PUB-HS TCRs were structurally related and contained shared core-sequences in their TCR-sequences. We found a prevalence of PUB-I and PUB-HS TCRs of up to 50% among individuals and showed frequencies of virus-specific PUB-I and PUB-HS TCRs making up more than 10% of each virus-specific T-cell population. These findings were confirmed by using an independent TCR-database of virus-specific TCRs. We therefore conclude that the magnitude of the contribution of PUB-I and PUB-HS TCRs to these virus-specific T-cell responses is high. Because the T cells from these virus-specific memory TCR-repertoires were the result of successful control of the virus in these healthy individuals, these PUB-HS TCRs and PUB-I TCRs may be attractive candidates for immunotherapy in immunocompromised patients that lack virus-specific T cells to control viral reactivation.

Abacavir inhibits but does not cause self-reactivity to HLA-B*57:01-restricted EBV specific T cell receptors

Pre-existing pathogen-specific memory T cell responses can contribute to multiple adverse outcomes including autoimmunity and drug hypersensitivity. How the specificity of the T cell receptor (TCR) is subverted or seconded in many of these diseases remains unclear. Here, we apply abacavir hypersensitivity (AHS) as a model to address this question because the disease is linked to memory T cell responses and the HLA risk allele, HLA-B*57:01, and the initiating insult, abacavir, are known. To investigate the role of pathogen-specific TCR specificity in mediating AHS we performed a genome-wide screen for HLA-B*57:01 restricted T cell responses to Epstein-Barr virus (EBV), one of the most prevalent human pathogens. T cell epitope mapping revealed HLA-B*57:01 restricted responses to 17 EBV open reading frames and identified an epitope encoded by EBNA3C. Using these data, we cloned the dominant TCR for EBNA3C and a previously defined epitope within EBNA3B. TCR specificity to each epitope was confirmed, however, cloned TCRs did not cross-react with abacavir plus self-peptide. Nevertheless, abacavir inhibited TCR interactions with their cognate ligands, demonstrating that TCR specificity may be subverted by a drug molecule. These results provide an experimental road map for future studies addressing the heterologous immune responses of TCRs including T cell mediated adverse drug reactions.

Cross-reactivity influences changes in human influenza A virus and Epstein Barr virus specific CD8 memory T cell receptor alpha and beta repertoires between young and old

Older people have difficulty controlling infection with common viruses such as influenza A virus (IAV), RNA virus which causes recurrent infections due to a high rate of genetic mutation, and Epstein Barr virus (EBV), DNA virus which persists in B cells for life in the 95% of people that become acutely infected. We questioned whether changes in epitope-specific memory CD8 T cell receptor (TCR) repertoires to these two common viruses could occur with increasing age and contribute to waning immunity. We compared CD8 memory TCR alpha and beta repertoires in two HLA-A2+ EBV- and IAV-immune donors, young (Y) and older (O) donors to three immunodominant epitopes known to be cross-reactive, IAV-M1_58-66 (IAV-M1), EBV-BMLF1_280-288 (EBV-BM), and EBV-BRLF1_109-117 (EBV-BR). We, therefore, also designed these studies to examine if TCR cross-reactivity could contribute to changes in repertoire with increasing age. TCR high throughput sequencing showed a significant difference in the pattern of TRBV usage between Y and O. However, there were many more differences in AV and AJ usage, between the age groups suggesting that changes in TCRα usage may play a greater role in evolution of the TCR repertoire emphasizing the importance of studying TRAV repertoires. With increasing age there was a preferential retention of TCR for all three epitopes with features in their complementarity-determining region (CDR3) that increased their ease of generation, and their cross-reactive potential. Young and older donors differed in the patterns of AV/AJ and BV/BJ pairings and usage of dominant CDR3 motifs specific to all three epitopes. Both young and older donors had cross-reactive responses between these 3 epitopes, which were unique and differed from the cognate responses having features that suggested they could interact with either ligand. There was an increased tendency for the classic IAV-M1 specific clone BV19-IRSS-JB2.7/AV27-CAGGGSQGNLIF-AJ42 to appear among the cross-reactive clones, suggesting that the dominance of this clone may relate to its cross-reactivity with EBV. These results suggest that although young and older donors retain classic TCR features for each epitope their repertoires are gradually changing with age, maintaining TCRs that are cross-reactive between these two common human viruses, one with recurrent infections and the other a persistent virus which frequently reactivates.

Intratumoral T-cell receptor repertoire is predictive of interim PET scan results in patients with diffuse large B-cell lymphoma treated with rituximab/cyclophosphamide/doxorubicin/prednisolone/vincristine (R-CHOP) chemoimmunotherapy

Objectives

A diverse intratumoral T‐cell receptor (TCR) repertoire is associated with improved survival in diffuse large B‐cell lymphoma (DLBCL) treated with rituximab/cyclophosphamide/doxorubicin/prednisolone/vincristine (R‐CHOP) chemoimmunotherapy. We explored the impact of intratumoral TCR repertoire on interim PET (iPET) done after four cycles of R‐CHOP, the relationships between intratumoral and circulating repertoire, and the phenotypes of expanded clonotypes.

Methods

We sequenced the third complementarity‐determining region of TCRβ in tumor samples, blood at pre‐therapy and after four cycles of R‐CHOP in 35 patients enrolled in ALLGNHL21 trial in high‐risk DLBCL. We correlated the TCR diversity metrics with iPET status, gene expression profiles and HLA‐class I genotypes. We then sequenced the FACS‐sorted peripheral blood T cells in six patients, and pentamer‐sorted EBV‐specific CD8⁺ T cells in one patient from this cohort.

Results

Compared with iPET⁻ patients, the intratumoral TCR repertoire in iPET⁺ patients was characterised by higher cumulative frequency of abundant clonotypes and higher productive clonality. There was a variable overlap between circulating and intratumoral repertoires, with the dominant intratumoral clonotypes more likely to be detected in the blood. The majority of shared clonotypes were CD8⁺ PD‐1^HI T cells, and CD8⁺ T cells had the largest clonal expansions in tumor and blood. In a patient with EBV⁺ DLBCL, EBV‐specific intratumoral clonotypes were trackable in the blood.

Conclusion

This study demonstrates that clonally expanded intratumoral TCR repertoires are associated with iPET⁺ and that the blood can be used to track tumor‐associated antigen‐specific clonotypes. These findings assist the rationale design and therapeutic monitoring of immunotherapeutic strategies in DLBCL.

SwarmTCR: a computational approach to predict the specificity of T cell receptors

Background

With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to predict T cell receptor specificity is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance.

Results

We compared the performance of SwarmTCR against another nearest-neighbor method and showed that SwarmTCR performs well both with bulk sequencing data and with single cell data. In addition, we show that the weights returned by SwarmTCR are biologically interpretable.

Conclusions

Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04335-w.

A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

T cell receptor (TCR) antigen-specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide-major histocompatibility complex) interactions and a neural network-based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen-specific interactions for basic immunological research and clinical immune monitoring.

Clinical Utility of In-house Metagenomic Next-generation Sequencing for the Diagnosis of Lower Respiratory Tract Infections and Analysis of the Host Immune Response

BACKGROUND

Only few pathogens that cause lower respiratory tract infections (LRTIs) can be identified due to limitations of traditional microbiological methods and the complexity of the oropharyngeal normal flora. Metagenomic next-generation sequencing (mNGS) has the potential to solve this problem.

METHODS

This prospective observational study sequentially enrolled 93 patients with LRTI and 69 patients without LRTI who visited Peking University People's Hospital in 2019. Pathogens in bronchoalveolar lavage fluid (BALF) specimens were detected using mNGS (DNA and RNA) and traditional microbiological assays. Human transcriptomes were compared between LRTI and non-LRTI, bacterial and viral LRTI, and tuberculosis and nontuberculosis groups.

RESULTS

Among 93 patients with LRTI, 20%, 35%, and 65% of cases were detected as definite or probable pathogens by culture, all microbiological tests, and mNGS, respectively. Our in-house BALF mNGS platform had an approximately 2-working-day turnaround time and detected more viruses and fungi than the other methods. Taking the composite reference standard as a gold standard, it had a sensitivity of 66.7%, specificity of 75.4%, positive-predictive value of 78.5%, and negative-predictive value of 62.7%. LRTI-, viral LRTI-, and tuberculosis-related differentially expressed genes were respectively related to immunity responses to infection, viral transcription and response to interferon-γ pathways, and perforin 1 and T-cell receptor B variable 9.

CONCLUSIONS

Metagenomic DNA and RNA-seq can identify a wide range of LRTI pathogens, with improved sensitivity for viruses and fungi. Our in-host platform is likely feasible in the clinic. Host transcriptome data are expected to be useful for the diagnosis of LRTIs.

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.

Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes

Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide-MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide-MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide-MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.

Epstein-Barr Virus Epitope-Major Histocompatibility Complex Interaction Combined with Convergent Recombination Drives Selection of Diverse T Cell Receptor α and β Repertoires

Recognition modes of individual T cell receptors (TCRs) are well studied, but factors driving the selection of TCR repertoires from primary through persistent human virus infections are less well understood. Using deep sequencing, we demonstrate a high degree of diversity of Epstein-Barr virus (EBV)-specific clonotypes in acute infectious mononucleosis (AIM). Only 9% of unique clonotypes detected in AIM persisted into convalescence; the majority (91%) of unique clonotypes detected in AIM were not detected in convalescence and were seeming replaced by equally diverse "de novo" clonotypes. The persistent clonotypes had a greater probability of being generated than nonpersistent clonotypes due to convergence recombination of multiple nucleotide sequences to encode the same amino acid sequence, as well as the use of shorter complementarity-determining regions 3 (CDR3s) with fewer nucleotide additions (i.e., sequences closer to germ line). Moreover, the two most immunodominant HLA-A2-restricted EBV epitopes, BRLF1109 and BMLF1280, show highly distinct antigen-specific public (i.e., shared between individuals) features. In fact, TCRα CDR3 motifs played a dominant role, while TCRβ played a minimal role, in the selection of TCR repertoire to an immunodominant EBV epitope, BRLF1. This contrasts with the majority of previously reported repertoires, which appear to be selected either on TCRβ CDR3 interactions with peptide/major histocompatibility complex (MHC) or in combination with TCRα CDR3. Understanding of how TCR-peptide-MHC complex interactions drive repertoire selection can be used to develop optimal strategies for vaccine design or generation of appropriate adoptive immunotherapies for viral infections in transplant settings or for cancer.IMPORTANCE Several lines of evidence suggest that TCRα and TCRβ repertoires play a role in disease outcomes and treatment strategies during viral infections in transplant patients and in cancer and autoimmune disease therapy. Our data suggest that it is essential that we understand the basic principles of how to drive optimum repertoires for both TCR chains, α and β. We address this important issue by characterizing the CD8 TCR repertoire to a common persistent human viral infection (EBV), which is controlled by appropriate CD8 T cell responses. The ultimate goal would be to determine if the individuals who are infected asymptomatically develop a different TCR repertoire than those that develop the immunopathology of AIM. Here, we begin by doing an in-depth characterization of both CD8 T cell TCRα and TCRβ repertoires to two immunodominant EBV epitopes over the course of AIM, identifying potential factors that may be driving their selection.