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Perez MAS, Chiffelle J, Bobisse S, Mayol-Rullan F, Bugnon M, Bragina ME, Arnaud M, Sauvage C, Barras D, Laniti DD, Huber F, Bassani-Sternberg M, Coukos G, Harari A, Zoete V. Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2405949. [PMID: 39159239 DOI: 10.1002/advs.202405949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/19/2024] [Indexed: 08/21/2024]
Abstract
Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.
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Affiliation(s)
- Marta A S Perez
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland
| | - Johanna Chiffelle
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - Sara Bobisse
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - Francesca Mayol-Rullan
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland
| | - Marine Bugnon
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland
| | - Maiia E Bragina
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland
| | - Marion Arnaud
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - Christophe Sauvage
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - David Barras
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - Denarda Dangaj Laniti
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - Florian Huber
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - George Coukos
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
- Department of Oncology, Immuno-Oncology Service, Lausanne University Hospital, Lausanne, CH-1011, Switzerland
| | - Alexandre Harari
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland
| | - Vincent Zoete
- Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland
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2
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Ehrlich R, Glynn E, Singh M, Ghersi D. Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity. Annu Rev Biomed Data Sci 2024; 7:295-316. [PMID: 38748864 DOI: 10.1146/annurev-biodatasci-102423-122741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
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.
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Affiliation(s)
- Ryan Ehrlich
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
| | - Eric Glynn
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA;
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Dario Ghersi
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
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3
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Zhang M, Cheng Q, Wei Z, Xu J, Wu S, Xu N, Zhao C, Yu L, Feng W. BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire. Brief Bioinform 2024; 25:bbae420. [PMID: 39177262 PMCID: PMC11342255 DOI: 10.1093/bib/bbae420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/24/2024] [Accepted: 08/08/2024] [Indexed: 08/24/2024] Open
Abstract
The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.
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Affiliation(s)
- Min Zhang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Qi Cheng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Zhenyu Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Jiayu Xu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Shiwei Wu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Nan Xu
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, No. 500 Dongchuan Road, Shanghai, 200241, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Chengkui Zhao
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Lei Yu
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, No. 500 Dongchuan Road, Shanghai, 200241, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Weixing Feng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
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4
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Karnaukhov VK, Shcherbinin DS, Chugunov AO, Chudakov DM, Efremov RG, Zvyagin IV, Shugay M. Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen. NATURE COMPUTATIONAL SCIENCE 2024; 4:510-521. [PMID: 38987378 DOI: 10.1038/s43588-024-00653-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 06/04/2024] [Indexed: 07/12/2024]
Abstract
T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.
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MESH Headings
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/chemistry
- Receptors, Antigen, T-Cell/metabolism
- Humans
- Peptides/immunology
- Peptides/chemistry
- Epitopes/immunology
- Epitopes/chemistry
- Models, Molecular
- Neoplasms/immunology
- Epitopes, T-Lymphocyte/immunology
- Epitopes, T-Lymphocyte/chemistry
- Major Histocompatibility Complex/immunology
- Protein Conformation
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
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Affiliation(s)
- Vadim K Karnaukhov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.
| | - Dmitrii S Shcherbinin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Anton O Chugunov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Dmitriy M Chudakov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia.
- Central European Institute of Technology, Brno, Czech Republic.
| | - Roman G Efremov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Higher School of Economics, Moscow, Russia
| | - Ivan V Zvyagin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Mikhail Shugay
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia.
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5
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Wang A, Lin X, Chau KN, Onuchic JN, Levine H, George JT. RACER-m leverages structural features for sparse T cell specificity prediction. SCIENCE ADVANCES 2024; 10:eadl0161. [PMID: 38748791 PMCID: PMC11095454 DOI: 10.1126/sciadv.adl0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
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Affiliation(s)
- Ailun Wang
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Xingcheng Lin
- Department of Physics, North Carolina State University, Raleigh, NC, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Kevin Ng Chau
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - José N. Onuchic
- Departments of Physics and Astronomy, Chemistry, and Biosciences, Rice University, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Jason T. George
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Biomedical Engineering, Texas A&M University, Houston, TX, USA
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6
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Jeffreys N, Brockman JM, Zhai Y, Ingber DE, Mooney DJ. Mechanical forces amplify TCR mechanotransduction in T cell activation and function. APPLIED PHYSICS REVIEWS 2024; 11:011304. [PMID: 38434676 PMCID: PMC10848667 DOI: 10.1063/5.0166848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 12/08/2023] [Indexed: 03/05/2024]
Abstract
Adoptive T cell immunotherapies, including engineered T cell receptor (eTCR) and chimeric antigen receptor (CAR) T cell immunotherapies, have shown efficacy in treating a subset of hematologic malignancies, exhibit promise in solid tumors, and have many other potential applications, such as in fibrosis, autoimmunity, and regenerative medicine. While immunoengineering has focused on designing biomaterials to present biochemical cues to manipulate T cells ex vivo and in vivo, mechanical cues that regulate their biology have been largely underappreciated. This review highlights the contributions of mechanical force to several receptor-ligand interactions critical to T cell function, with central focus on the TCR-peptide-loaded major histocompatibility complex (pMHC). We then emphasize the role of mechanical forces in (i) allosteric strengthening of the TCR-pMHC interaction in amplifying ligand discrimination during T cell antigen recognition prior to activation and (ii) T cell interactions with the extracellular matrix. We then describe approaches to design eTCRs, CARs, and biomaterials to exploit TCR mechanosensitivity in order to potentiate T cell manufacturing and function in adoptive T cell immunotherapy.
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Affiliation(s)
| | | | - Yunhao Zhai
- Wyss Institute for Biologically Inspired Engineering, Boston, Massachusetts 02115, USA
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7
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Tripathi S, Tsang JS, Park K. Systems immunology of regulatory T cells: can one circuit explain it all? Trends Immunol 2023; 44:766-781. [PMID: 37690962 PMCID: PMC10543564 DOI: 10.1016/j.it.2023.08.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 09/12/2023]
Abstract
Regulatory T (Treg) cells play vital roles in immune homeostasis and response, including discrimination between self- and non-self-antigens, containment of immunopathology, and inflammation resolution. These diverse functions are orchestrated by cellular circuits involving Tregs and other cell types across space and time. Despite dramatic progress in our understanding of Treg biology, a quantitative framework capturing how Treg-containing circuits give rise to these diverse functions is lacking. Here, we propose that different facets of Treg function can be interpreted as distinct operating regimes of the same underlying circuit. We discuss how a systems immunology approach, involving quantitative experiments, computational modeling, and machine learning, can advance our understanding of Treg function, and help identify general operating and design principles underlying immune regulation.
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Affiliation(s)
- Shubham Tripathi
- Yale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA.
| | - John S Tsang
- Yale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA.
| | - Kyemyung Park
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA; Graduate School of Health Science and Technology and Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
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8
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Bravi B, Di Gioacchino A, Fernandez-de-Cossio-Diaz J, Walczak AM, Mora T, Cocco S, Monasson R. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife 2023; 12:e85126. [PMID: 37681658 PMCID: PMC10522340 DOI: 10.7554/elife.85126] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 09/07/2023] [Indexed: 09/09/2023] Open
Abstract
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Andrea Di Gioacchino
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Jorge Fernandez-de-Cossio-Diaz
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Aleksandra M Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Simona Cocco
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
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9
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Ghoreyshi ZS, George JT. Quantitative approaches for decoding the specificity of the human T cell repertoire. Front Immunol 2023; 14:1228873. [PMID: 37781387 PMCID: PMC10539903 DOI: 10.3389/fimmu.2023.1228873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023] Open
Abstract
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.
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Affiliation(s)
- Zahra S. Ghoreyshi
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Jason T. George
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Engineering Medicine Program, Texas A&M University, Houston, TX, United States
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
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10
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Zhao Y, He B, Xu F, Li C, Xu Z, Su X, He H, Huang Y, Rossjohn J, Song J, Yao J. DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis. SCIENCE ADVANCES 2023; 9:eabo5128. [PMID: 37556545 PMCID: PMC10411891 DOI: 10.1126/sciadv.abo5128] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/06/2023] [Indexed: 08/11/2023]
Abstract
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.
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Affiliation(s)
- Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Bing He
- AI Lab, Tencent, Shenzhen, China
| | - Fan Xu
- AI Lab, Tencent, Shenzhen, China
| | - Chen Li
- Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | | | | | | | | | - Jamie Rossjohn
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
- Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff, UK
| | - Jiangning Song
- AI Lab, Tencent, Shenzhen, China
- Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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11
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Rappazzo CG, Fernández-Quintero ML, Mayer A, Wu NC, Greiff V, Guthmiller JJ. Defining and Studying B Cell Receptor and TCR Interactions. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 211:311-322. [PMID: 37459189 PMCID: PMC10495106 DOI: 10.4049/jimmunol.2300136] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/15/2023] [Indexed: 07/20/2023]
Abstract
BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.
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Affiliation(s)
| | | | - Andreas Mayer
- Division of Infection and Immunity, University College London, London WC1E 6BT, UK
| | - Nicholas C. Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372 Oslo, Norway
| | - Jenna J. Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
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12
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Fonseca AF, Antunes DA. CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases. Front Immunol 2023; 14:1142573. [PMID: 37377956 PMCID: PMC10291144 DOI: 10.3389/fimmu.2023.1142573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.
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Affiliation(s)
| | - Dinler A. Antunes
- Antunes Lab, Center for Nuclear Receptors and Cell Signaling (CNRCS), Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
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13
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Pham MDN, Nguyen TN, Tran LS, Nguyen QTB, Nguyen TPH, Pham TMQ, Nguyen HN, Giang H, Phan MD, Nguyen V. epiTCR: a highly sensitive predictor for TCR-peptide binding. Bioinformatics 2023; 39:btad284. [PMID: 37094220 PMCID: PMC10159657 DOI: 10.1093/bioinformatics/btad284] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 02/28/2023] [Accepted: 04/22/2023] [Indexed: 04/26/2023] Open
Abstract
MOTIVATION Predicting the binding between T-cell receptor (TCR) and peptide presented by human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed. RESULTS We collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of >3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3β sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with area under the curve (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools, thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy. AVAILABILITY AND IMPLEMENTATION epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR).
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Affiliation(s)
| | | | - Le Son Tran
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
- NexCalibur Therapeutics, Wilmington, DE, United States
| | | | | | | | - Hoai-Nghia Nguyen
- NexCalibur Therapeutics, Wilmington, DE, United States
- University of Medicine & Pharmacy, Ho Chi Minh City, Vietnam
| | - Hoa Giang
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
- NexCalibur Therapeutics, Wilmington, DE, United States
| | - Minh-Duy Phan
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
- NexCalibur Therapeutics, Wilmington, DE, United States
| | - Vy Nguyen
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
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14
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Peng X, Lei Y, Feng P, Jia L, Ma J, Zhao D, Zeng J. Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00634-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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15
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Fang Y, Liu X, Liu H. Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity. Brief Bioinform 2022; 23:6696141. [PMID: 36094087 DOI: 10.1093/bib/bbac378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/06/2022] [Accepted: 08/09/2022] [Indexed: 12/14/2022] Open
Abstract
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.
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Affiliation(s)
- Yiming Fang
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
| | - Xuejun Liu
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China
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16
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Ng Chau K, George JT, Onuchic JN, Lin X, Levine H. Contact map dependence of a T-cell receptor binding repertoire. Phys Rev E 2022; 106:014406. [PMID: 35974642 DOI: 10.1103/physreve.106.014406] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
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.
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Affiliation(s)
- Kevin Ng Chau
- Physics Department, Northeastern University, Boston, Massachusetts 02115, USA
| | - Jason T George
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA
| | - José N Onuchic
- Center for Theoretical Biological Physics and Departments of Physics and Astronomy, Chemistry and Biosciences, Rice University, Houston, Texas 77005, USA
| | - Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics and Departments of Physics and Bioengineering, Northeastern University, Boston, Massachusetts 02115, USA
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17
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Yang J, Li Y, Ye J, Wang J, Lu H, Yao X. Characterization of the TCR β Chain Repertoire in Peripheral Blood from Hepatitis B Vaccine Responders and Non-Responders. J Inflamm Res 2022; 15:939-951. [PMID: 35210805 PMCID: PMC8856041 DOI: 10.2147/jir.s347702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/26/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Hepatitis B (HepB) vaccination can effectively prevent the prevalence of hepatitis B virus (HBV) infection. However, the incidence of vaccination failure is about 5~10% and the underlying molecular mechanisms are poorly understood. T cells have an essential role in the recipient's immune response to vaccine, which could be elucidated by high-throughput sequencing (HTS) and bioinformatics analysis. METHODS We conducted HTS of the T cell receptor β chain (TRB) complementarity-determining region 3 (CDR3) repertoires in eighteen positive responders (responders) and 10 negative responders (non-responders) who all had HepB vaccination, the repertoire features of BV, BJ and V-J genes and their diversity, respectively, were compared between the positive and negative responders using the Mann-Whitney test. Moreover, the relatively conserved motifs in CDR3 were revealed and compared to those in the other group's report. RESULTS The diversity of TRB CDR3 and the frequencies of BV27 and BV7-9 are significantly increased for HepB vaccine responders compared to those in non-responders. The motifs of CDR3s in BV27/J1-1, BV27/J2-5, and BV7-9/J2-5, respectively, were most expressed as "NTE", "QETQ", and "GG-Q (E)-ETQ". Moreover, the motif "KLNSPL" was determined in nearly 80% CDR3s in BV27/J1-6 from HepB vaccine responders for the first time. CONCLUSION Our results present the comprehensive profiles of TRB CDR3 in the HepB vaccine responders and non-responders after standard vaccination protocol and determine the relatively conservative motifs of CDR3s that may respond to the HepB vaccine. Further results suggest that the profile of TRB repertoire could distinguish the HepB vaccine responders from non-responders and provide a new target for optimizing and improving the efficiency of the HepB vaccine.
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Affiliation(s)
- Jiezuan Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Infectious Diseases, Hangzhou, People’s Republic of China
| | - Yongtao Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Infectious Diseases, Hangzhou, People’s Republic of China
| | - Jing Ye
- Department of Surgical ICU, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, People’s Republic of China
| | - Ju Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Infectious Diseases, Hangzhou, People’s Republic of China
| | - Haifeng Lu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Infectious Diseases, Hangzhou, People’s Republic of China
| | - Xinsheng Yao
- Department of Immunology, Research Center for Medicine & Biology, Innovation & Practice Base for Graduate Students Education, Zunyi Medical University, Zunyi, People’s Republic of China
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18
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Milighetti M, Shawe-Taylor J, Chain B. Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes. Front Physiol 2021; 12:730908. [PMID: 34566692 PMCID: PMC8456106 DOI: 10.3389/fphys.2021.730908] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/02/2021] [Indexed: 11/13/2022] Open
Abstract
The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.
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Affiliation(s)
- Martina Milighetti
- Division of Infection and Immunity, University College London, London, United Kingdom
- Cancer Institute, University College London, London, United Kingdom
| | - John Shawe-Taylor
- Department of Computer Science, University College London, London, United Kingdom
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
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