1
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Chi H, Pepper M, Thomas PG. Principles and therapeutic applications of adaptive immunity. Cell 2024; 187:2052-2078. [PMID: 38670065 DOI: 10.1016/j.cell.2024.03.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/01/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024]
Abstract
Adaptive immunity provides protection against infectious and malignant diseases. These effects are mediated by lymphocytes that sense and respond with targeted precision to perturbations induced by pathogens and tissue damage. Here, we review key principles underlying adaptive immunity orchestrated by distinct T cell and B cell populations and their extensions to disease therapies. We discuss the intracellular and intercellular processes shaping antigen specificity and recognition in immune activation and lymphocyte functions in mediating effector and memory responses. We also describe how lymphocytes balance protective immunity against autoimmunity and immunopathology, including during immune tolerance, response to chronic antigen stimulation, and adaptation to non-lymphoid tissues in coordinating tissue immunity and homeostasis. Finally, we discuss extracellular signals and cell-intrinsic programs underpinning adaptive immunity and conclude by summarizing key advances in vaccination and engineering adaptive immune responses for therapeutic interventions. A deeper understanding of these principles holds promise for uncovering new means to improve human health.
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Affiliation(s)
- Hongbo Chi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA.
| | - Marion Pepper
- Department of Immunology, University of Washington, Seattle, WA, USA.
| | - Paul G Thomas
- Department of Host-Microbe Interactions and Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA.
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2
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Shimizu T, Sun L, Ohnishi K. Influence of pre-B cell receptor deficiency on the immunoglobulin repertoires in peripheral blood B cells before and after immunization. Mol Immunol 2024; 166:87-100. [PMID: 38271880 DOI: 10.1016/j.molimm.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/17/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
During B cell development, pre-B cell receptor (pre-BCR), comprising the immunoglobulin heavy chain (HC) and surrogate light chain (SLC), plays a crucial role. The expression of pre-BCR serves as a certification of HC quality, confirming its ability to associate with the SLC and light chain (LC). In mice lacking SLC, the absence of this quality control mechanism leads to a distorted repertoire of HCs in the spleen and bone marrow. In this study, we conducted a comparative analysis of the immunoglobulin gene repertoire in peripheral blood cells of both wild-type mice and pre-BCR-deficient mice. Our findings reveal differences not only in the μ HC repertoire but also in the α HC and κ LC repertoires of the pre-BCR-deficient mice. These results suggest that the pre-BCR-mediated quality check of HC influences the selection of class-switched HC and LC repertoires. To further explore the impact of pre-BCR deficiency, we immunized these mice with thymus-dependent antigens and compared the antigen-responding repertoires. Our observations indicate that the affinity maturation pathways remain consistent between wild-type mice and pre-BCR-deficient mice, albeit with variations in the degree of maturation.
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Affiliation(s)
- Takeyuki Shimizu
- Department of Immunology, Kochi Medical School, Kochi University, Nankoku, Kochi 783-8505, Japan.
| | - Lin Sun
- Department of Immunology, National Institute of Infectious Diseases, Shinjuku, Tokyo 162-8640, Japan
| | - Kazuo Ohnishi
- Department of Immunology, National Institute of Infectious Diseases, Shinjuku, Tokyo 162-8640, Japan
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3
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Abstract
Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.
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Affiliation(s)
- Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, AB, T6G-2E1, Canada.
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G-2E9, Canada.
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4
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Tippalagama R, Chihab LY, Kearns K, Lewis S, Panda S, Willemsen L, Burel JG, Lindestam Arlehamn CS. Antigen-specificity measurements are the key to understanding T cell responses. Front Immunol 2023; 14:1127470. [PMID: 37122719 PMCID: PMC10140422 DOI: 10.3389/fimmu.2023.1127470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/30/2023] [Indexed: 05/02/2023] Open
Abstract
Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.
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5
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Vujović M, Marcatili P, Chain B, Kaplinsky J, Andresen TL. Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm. Commun Biol 2023; 6:357. [PMID: 37002292 PMCID: PMC10066310 DOI: 10.1038/s42003-023-04702-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
Changes in the T cell receptor (TCR) repertoires have become important markers for monitoring disease or therapy progression. With the rise of immunotherapy usage in cancer, infectious and autoimmune disease, accurate assessment and comparison of the "state" of the TCR repertoire has become paramount. One important driver of change within the repertoire is T cell proliferation following immunisation. A way of monitoring this is by investigating large clones of individual T cells believed to bind epitopes connected to the disease. However, as a single target can be bound by many different TCRs, monitoring individual clones cannot fully account for T cell cross-reactivity. Moreover, T cells responding to the same target often exhibit higher sequence similarity, which highlights the importance of accounting for TCR similarity within the repertoire. This complexity of binding relationships between a TCR and its target convolutes comparison of immune responses between individuals or comparisons of TCR repertoires at different timepoints. Here we propose TCRDivER algorithm (T cell Receptor Diversity Estimates for Repertoires), a global method of T cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. This approach allowed for distinction between spleen TCR repertoires of immunised and non-immunised mice, showing the need for including both facets of repertoire changes simultaneously. The analysis revealed biologically interpretable relationships between sequence similarity and clonality. These aid in understanding differences and separation of repertoires stemming from different biological context. With the rise of availability of sequencing data we expect our tool to find broad usage in clinical and research applications.
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Affiliation(s)
- Milena Vujović
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
| | - Paolo Marcatili
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
| | - Benny Chain
- UCL Division of Infection and Immunity, University College London, London, UK.
| | - Joseph Kaplinsky
- Ludwig Institute for Cancer Research Ltd, University of Oxford, Nuffield Department of Medicine, Oxford, UK.
| | - Thomas Lars Andresen
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
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6
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Abstract
•T-cell receptor (TCR) interaction with major histocompatibility complex-antigen complexes leads to antitumour responses.•TCR sequencing analysis allows characterisation of T cells that recognise tumour neoantigens.•T-cell clonal revival and clonal replacement potentially underpin immunotherapy responses.
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Affiliation(s)
- Á F Sanromán
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - K Joshi
- Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London, UK.,Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - L Au
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia.,Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Australia
| | - B Chain
- Division of Infection and Immunity, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - S Turajlic
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK.,Melanoma and Kidney Cancer Team, The Institute of Cancer Research, London, UK
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7
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Camaglia F, Ryvkin A, Greenstein E, Reich-Zeliger S, Chain B, Mora T, Walczak AM, Friedman N. Quantifying changes in the T cell receptor repertoire during thymic development. eLife 2023; 12:81622. [PMID: 36661220 PMCID: PMC9934861 DOI: 10.7554/elife.81622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
One of the feats of adaptive immunity is its ability to recognize foreign pathogens while sparing the self. During maturation in the thymus, T cells are selected through the binding properties of their antigen-specific T-cell receptor (TCR), through the elimination of both weakly (positive selection) and strongly (negative selection) self-reactive receptors. However, the impact of thymic selection on the TCR repertoire is poorly understood. Here, we use transgenic Nur77-mice expressing a T-cell activation reporter to study the repertoires of thymic T cells at various stages of their development, including cells that do not pass selection. We combine high-throughput repertoire sequencing with statistical inference techniques to characterize the selection of the TCR in these distinct subsets. We find small but significant differences in the TCR repertoire parameters between the maturation stages, which recapitulate known differentiation pathways leading to the CD4+ and CD8+ subtypes. These differences can be simulated by simple models of selection acting linearly on the sequence features. We find no evidence of specific sequences or sequence motifs or features that are suppressed by negative selection. These results favour a collective or statistical model for T-cell self non-self discrimination, where negative selection biases the repertoire away from self recognition, rather than ensuring lack of self-reactivity at the single-cell level.
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Affiliation(s)
- Francesco Camaglia
- Laboratoire de physique de l’École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de ParisParisFrance
| | - Arie Ryvkin
- Department of Immunology, Weizmann Institute of ScienceRehovotIsrael
| | - Erez Greenstein
- Department of Immunology, Weizmann Institute of ScienceRehovotIsrael
| | | | - Benny Chain
- Division of Infection and Immunity, University College LondonLondonUnited Kingdom
| | - Thierry Mora
- Laboratoire de physique de l’École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de ParisParisFrance
| | - Aleksandra M Walczak
- Laboratoire de physique de l’École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de ParisParisFrance
| | - Nir Friedman
- Department of Immunology, Weizmann Institute of ScienceRehovotIsrael
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8
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Ostmeyer J, Cowell L, Christley S. Dynamic kernel matching for non-conforming data: A case study of T cell receptor datasets. PLoS One 2023; 18:e0265313. [PMID: 36881590 PMCID: PMC9990938 DOI: 10.1371/journal.pone.0265313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 03/01/2022] [Indexed: 03/08/2023] Open
Abstract
Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an approach for modifying established statistical classifiers to handle non-conforming data, which we call dynamic kernel matching (DKM). As examples of non-conforming data, we consider (i) a dataset of T-cell receptor (TCR) sequences labelled by disease antigen and (ii) a dataset of sequenced TCR repertoires labelled by patient cytomegalovirus (CMV) serostatus, anticipating that both datasets contain signatures for diagnosing disease. We successfully fit statistical classifiers augmented with DKM to both datasets and report the performance on holdout data using standard metrics and metrics allowing for indeterminant diagnoses. Finally, we identify the patterns used by our statistical classifiers to generate predictions and show that these patterns agree with observations from experimental studies.
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Affiliation(s)
- Jared Ostmeyer
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail:
| | - Lindsay Cowell
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Scott Christley
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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9
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Kanduri C, Scheffer L, Pavlović M, Rand KD, Chernigovskaya M, Pirvandy O, Yaari G, Greiff V, Sandve GK. simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods. Gigascience 2022; 12:giad074. [PMID: 37848619 PMCID: PMC10580376 DOI: 10.1093/gigascience/giad074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/20/2023] [Accepted: 08/29/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires. RESULTS We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets. CONCLUSIONS This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.
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Affiliation(s)
- Chakravarthi Kanduri
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
| | - Lonneke Scheffer
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Milena Pavlović
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
| | - Knut Dagestad Rand
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway
| | - Oz Pirvandy
- Faculty of Engineering, Bar-Ilan University, 5290002, Israel
| | - Gur Yaari
- Faculty of Engineering, Bar-Ilan University, 5290002, Israel
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway
| | - Geir K Sandve
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
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10
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Olson BJ, Schattgen SA, Thomas PG, Bradley P, Matsen IV FA. Comparing T cell receptor repertoires using optimal transport. PLoS Comput Biol 2022; 18:e1010681. [PMID: 36476997 PMCID: PMC9728925 DOI: 10.1371/journal.pcbi.1010681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/24/2022] [Indexed: 12/12/2022] Open
Abstract
The complexity of entire T cell receptor (TCR) repertoires makes their comparison a difficult but important task. Current methods of TCR repertoire comparison can incur a high loss of distributional information by considering overly simplistic sequence- or repertoire-level characteristics. Optimal transport methods form a suitable approach for such comparison given some distance or metric between values in the sample space, with appealing theoretical and computational properties. In this paper we introduce a nonparametric approach to comparing empirical TCR repertoires that applies the Sinkhorn distance, a fast, contemporary optimal transport method, and a recently-created distance between TCRs called TCRdist. We show that our methods identify meaningful differences between samples from distinct TCR distributions for several case studies, and compete with more complicated methods despite minimal modeling assumptions and a simpler pipeline.
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Affiliation(s)
- Branden J. Olson
- Department of Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Statistics, University of Washington, Seattle, Washington, United States of America
| | - Stefan A. Schattgen
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, Tennessee, United States of America
| | - Paul G. Thomas
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, Tennessee, United States of America
| | - Philip Bradley
- Department of Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Institute for Protein Design, Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- * E-mail: (PB); (FAM)
| | - Frederick A. Matsen IV
- Department of Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Statistics, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
- * E-mail: (PB); (FAM)
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11
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Weber CR, Rubio T, Wang L, Zhang W, Robert PA, Akbar R, Snapkov I, Wu J, Kuijjer ML, Tarazona S, Conesa A, Sandve GK, Liu X, Reddy ST, Greiff V. Reference-based comparison of adaptive immune receptor repertoires. Cell Rep Methods 2022; 2:100269. [PMID: 36046619 PMCID: PMC9421535 DOI: 10.1016/j.crmeth.2022.100269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/01/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022]
Abstract
B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.
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Affiliation(s)
- Cédric R. Weber
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Teresa Rubio
- Laboratory of Neurobiology, Centro Investigación Príncipe Felipe, Valencia, Spain
| | - Longlong Wang
- BGI-Shenzhen, Shenzhen, China
- BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Wei Zhang
- BGI-Shenzhen, Shenzhen, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, China
| | - Philippe A. Robert
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Igor Snapkov
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | | | - Marieke L. Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sonia Tarazona
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Valencia, Spain
| | - Geir K. Sandve
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Xiao Liu
- BGI-Shenzhen, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Sai T. Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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12
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Krishnamurthy K, Hermundstad AM, Mora T, Walczak AM, Balasubramanian V. Disorder and the Neural Representation of Complex Odors. Front Comput Neurosci 2022; 16:917786. [PMID: 36003684 PMCID: PMC9393645 DOI: 10.3389/fncom.2022.917786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022] Open
Abstract
Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from Drosophila. Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.
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Affiliation(s)
- Kamesh Krishnamurthy
- Joseph Henry Laboratories of Physics and Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Ann M. Hermundstad
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Thierry Mora
- Laboratoire de Physique Statistique, UMR8550, CNRS, UPMC and École Normale Supérieure, Paris, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique Théorique, UMR8549m CNRS, UPMC and École Normale Supérieure, Paris, France
| | - Vijay Balasubramanian
- David Rittenhouse and Richards Laboratories, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Vijay Balasubramanian
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13
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Mark M, Reich-Zeliger S, Greenstein E, Reshef D, Madi A, Chain B, Friedman N. A hierarchy of selection pressures determines the organization of the T cell receptor repertoire. Front Immunol 2022; 13:939394. [PMID: 35967295 PMCID: PMC9372880 DOI: 10.3389/fimmu.2022.939394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
We systematically examine the receptor repertoire in T cell subsets in young, adult, and LCMV-infected mice. Somatic recombination generates diversity, resulting in the limited overlap between nucleotide sequences of different repertoires even within the same individual. However, statistical features of the repertoire, quantified by the V gene and CDR3 k-mer frequency distributions, are highly conserved. A hierarchy of immunological processes drives the evolution of this structure. Intra-thymic divergence of CD4+ and CD8+ lineages imposes subtle but dominant differences observed across repertoires of all subpopulations in both young and adult mice. Differentiation from naive through memory to effector phenotype imposes an additional gradient of repertoire diversification, which is further influenced by age in a complex and lineage-dependent manner. The distinct repertoire of CD4+ regulatory T cells is more similar to naive cells in young mice and to effectors in adults. Finally, we describe divergent (naive and memory) and convergent (CD8+ effector) evolution of the repertoire following acute infection with LCMV. This study presents a quantitative framework that captures the structure of the repertoire in terms of its fundamental statistical properties and describes how this structure evolves as individual T cells differentiate, migrate and mature in response to antigen exposure.
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Affiliation(s)
- Michal Mark
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
- *Correspondence: Michal Mark, ; Benny Chain,
| | | | - Erez Greenstein
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Dan Reshef
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Asaf Madi
- Department of Pathology, Tel-Aviv University, Tel-Aviv, Israel
| | - Benny Chain
- Department of Computer Science, University College London, UCL, London, United Kingdom
- *Correspondence: Michal Mark, ; Benny Chain,
| | - Nir Friedman
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
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14
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Katayama Y, Kobayashi TJ. Comparative Study of Repertoire Classification Methods Reveals Data Efficiency of k-mer Feature Extraction. Front Immunol 2022; 13:797640. [PMID: 35936014 PMCID: PMC9346074 DOI: 10.3389/fimmu.2022.797640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 06/20/2022] [Indexed: 01/18/2023] Open
Abstract
The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing (NGS). In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that their performances can be impaired substantially below critical sample sizes. To complement this drawback, we propose MotifBoost, which exploits the information of short k-mer motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We tested MotifBoost on the four small datasets which consist of various conditions such as Cytomegalovirus (CMV), HIV, α-chain, β-chain and it consistently preserved the stability. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of k-mer motifs as representation features of repertoires. Finally, by comparing the predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.
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Affiliation(s)
- Yotaro Katayama
- Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- *Correspondence: Yotaro Katayama,
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15
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Kanduri C, Pavlović M, Scheffer L, Motwani K, Chernigovskaya M, Greiff V, Sandve GK. Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification. Gigascience 2022; 11:6593147. [PMID: 35639633 PMCID: PMC9154052 DOI: 10.1093/gigascience/giac046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 12/23/2021] [Accepted: 04/08/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required. RESULTS To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state-associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences. CONCLUSIONS We provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.
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Affiliation(s)
- Chakravarthi Kanduri
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
| | - Milena Pavlović
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
| | - Lonneke Scheffer
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
| | - Keshav Motwani
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, FL 32610, USA
| | - Maria Chernigovskaya
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, 0372, Norway
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, 0372, Norway
| | - Geir K Sandve
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
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16
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Wang J, Jiang D, Zheng X, Li W, Zhao T, Wang D, Yu H, Sun D, Li Z, Zhang J, Zhang Z, Hou L, Jiang G, Fei K, Zhang F, Yang K, Zhang P. Tertiary lymphoid structure and decreased CD8+ T cell infiltration in minimally invasive adenocarcinoma. iScience 2022; 25:103883. [PMID: 35243243 PMCID: PMC8873609 DOI: 10.1016/j.isci.2022.103883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/03/2021] [Accepted: 02/02/2022] [Indexed: 12/13/2022] Open
Abstract
Knowledge of the tumor microenvironment (TME) in patients with early lung cancer, especially in comparison with the matched adjacent tissues, remains lacking. To characterize TME of early-stage lung adenocarcinoma, we performed RNA-seq profiling on 58 pairs of minimally invasive adenocarcinoma (MIA) tumors and matched adjacent normal tissues. MIA tumors exhibited an adaptive TME characterized by high CD4+ T cell infiltration, high B-cell activation, and low CD8+ T cell infiltration. The high expression of markers for B cells, activated CD4+ T cells, and follicular helper T (Tfh) cells in bulk MIA samples and three independent single-cell RNA-seq datasets implied tertiary lymphoid structures (TLS) formation. Multiplex immunohistochemistry staining validated TLS formation and revealed an enrichment of follicular regulatory T cells (Tfr) in TLS follicles, which may explain the lower CD8+ T cell infiltration and attenuated anti-tumor immunity in MIA. Our study demonstrates how integrating transcriptome and pathology characterize TME and elucidate potential mechanisms of tumor immune evasion. Higher infiltration and activation of B and CD4+ T cell characterize MIA tumors MIA tumors are infiltrated with lower CD8+ T cells than normal tissues TLS constituted by B, CD4+ T cells, and CD35+ FDC is validated in MIA tumors Decreased CD8+ T is associated with Tfr-mediated immunosuppression in MIA tumor
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Affiliation(s)
- Jin Wang
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Dongbo Jiang
- Department of Immunology, School of Basic Medicine, Air-Force Medical University (Fourth Military Medical University), Xi'an, China
| | - Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai, China
| | - Wang Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Life Sciences and Technology, Shanghai, China
| | - Tian Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Life Sciences and Technology, Shanghai, China
| | - Di Wang
- Tissue Bank, Department of Pathology, Experimental Animal Center, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huansha Yu
- Tissue Bank, Department of Pathology, Experimental Animal Center, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dongqing Sun
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Ziyi Li
- Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Jian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Zhe Zhang
- Department of Gynecologic Oncology, Chinese PLA General Hospital, Beijing, China
| | - Likun Hou
- Tissue Bank, Department of Pathology, Experimental Animal Center, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ke Fei
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fan Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Life Sciences and Technology, Shanghai, China
- Corresponding author
| | - Kun Yang
- Department of Immunology, School of Basic Medicine, Air-Force Medical University (Fourth Military Medical University), Xi'an, China
- Corresponding author
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
- Corresponding author
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17
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Joshi K, Milighetti M, Chain BM. Application of T cell receptor (TCR) repertoire analysis for the advancement of cancer immunotherapy. Curr Opin Immunol 2022; 74:1-8. [PMID: 34454284 DOI: 10.1016/j.coi.2021.07.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Kroopa Joshi
- Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Martina Milighetti
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Benjamin M 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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18
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Minervina AA, Pogorelyy MV, Kirk AM, Crawford JC, Allen EK, Chou CH, Mettelman RC, Allison KJ, Lin CY, Brice DC, Zhu X, Vegesana K, Wu G, Trivedi S, Kottapalli P, Darnell D, McNeely S, Olsen SR, Schultz-Cherry S, Estepp JH, McGargill MA, Wolf J, Thomas PG. SARS-CoV-2 antigen exposure history shapes phenotypes and specificity of memory CD8 T cells. medRxiv 2022:2021.07.12.21260227. [PMID: 34341799 PMCID: PMC8328067 DOI: 10.1101/2021.07.12.21260227] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Although mRNA vaccine efficacy against severe COVID-19 remains high, variant emergence and breakthrough infections have changed vaccine policy to include booster immunizations. However, the effect of diverse and repeated antigen exposures on SARS-CoV-2 memory T cells is poorly understood. Here, we utilize DNA-barcoded MHC-multimers combined with scRNAseq and scTCRseq to capture the ex vivo profile of SARS-CoV-2-responsive T cells within a cohort of individuals with one, two, or three antigen exposures, including vaccination, primary infection, and breakthrough infection. We found that the order of exposure determined the relative distribution between spike- and non-spike-specific responses, with vaccination after infection leading to further expansion of spike-specific T cells and differentiation to a CCR7-CD45RA+ effector phenotype. In contrast, individuals experiencing a breakthrough infection mount vigorous non-spike-specific responses. In-depth analysis of over 4,000 epitope-specific T cell receptor sequences demonstrates that all types of exposures elicit diverse repertoires characterized by shared, dominant TCR motifs, with no evidence for repertoire narrowing from repeated exposure. Our findings suggest that breakthrough infections diversify the T cell memory repertoire and that current vaccination protocols continue to expand and differentiate spike-specific memory responses.
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Affiliation(s)
| | - Mikhail V. Pogorelyy
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Allison M. Kirk
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | | | - E. Kaitlynn Allen
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Ching-Heng Chou
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Robert C. Mettelman
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Kim J. Allison
- Department of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Chun-Yang Lin
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - David C. Brice
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Xun Zhu
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Kasi Vegesana
- Information Services, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Gang Wu
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Sanchit Trivedi
- Hartwell Center for Bioinformatics & Biotechnology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Pratibha Kottapalli
- Hartwell Center for Bioinformatics & Biotechnology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Daniel Darnell
- Hartwell Center for Bioinformatics & Biotechnology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Suzanne McNeely
- Hartwell Center for Bioinformatics & Biotechnology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Scott R. Olsen
- Hartwell Center for Bioinformatics & Biotechnology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Stacey Schultz-Cherry
- Department of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Jeremie H. Estepp
- Department of Global Pediatric Medicine, St. Jude Children’s Research Hospital, Memphis, TN USA
| | | | - Maureen A. McGargill
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Joshua Wolf
- Department of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Paul G. Thomas
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN USA
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19
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Abstract
Grouping TCRs on the similarity of CDR3 sequences could effectively cluster them by specificity. Three versions of the GLIPH algorithm are described briefly here, with instructions to use GLIPH algorithms to cluster TCRs by specificity.
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Affiliation(s)
- Chunlin Wang
- Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
| | - Huang Huang
- Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Mark M Davis
- Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- The Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
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20
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Pavlović M, Scheffer L, Motwani K, Kanduri C, Kompova R, Vazov N, Waagan K, Bernal FLM, Costa AA, Corrie B, Akbar R, Al Hajj GS, Balaban G, Brusko TM, Chernigovskaya M, Christley S, Cowell LG, Frank R, Grytten I, Gundersen S, Haff IH, Hovig E, Hsieh P, Klambauer G, Kuijjer ML, Lund-andersen C, Martini A, Minotto T, Pensar J, Rand K, Riccardi E, Robert PA, Rocha A, Slabodkin A, Snapkov I, Sollid LM, Titov D, Weber CR, Widrich M, Yaari G, Greiff V, Sandve GK. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. NAT MACH INTELL 2021; 3:936-44. [DOI: 10.1038/s42256-021-00413-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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21
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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22
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Niebuhr M, Belde J, Fähnrich A, Serge A, Irla M, Ellebrecht CT, Hammers CM, Bieber K, Westermann J, Kalies K. Receptor repertoires of murine follicular T helper cells reveal a high clonal overlap in separate lymph nodes in autoimmunity. eLife 2021; 10:70053. [PMID: 34402793 PMCID: PMC8370764 DOI: 10.7554/elife.70053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 08/02/2021] [Indexed: 12/21/2022] Open
Abstract
Follicular T helper cells (Tfh) are a specialized subset of CD4 effector T cells that are crucial for germinal center (GC) reactions and for selecting B cells to undergo affinity maturation. Despite this central role for humoral immunity, only few data exist about their clonal distribution when multiple lymphoid organs are exposed to the same antigen (Ag) as it is the case in autoimmunity. Here, we used an autoantibody-mediated disease model of the skin and injected one auto-Ag into the two footpads of the same mouse and analyzed the T cell receptor (TCR)β sequences of Tfh located in GCs of both contralateral draining lymph nodes. We found that over 90% of the dominant GC-Tfh clonotypes were shared in both lymph nodes but only transiently. The initially dominant Tfh clonotypes especially declined after establishment of chronic disease while GC reaction and autoimmune disease continued. Our data demonstrates a dynamic behavior of Tfh clonotypes under autoimmune conditions and emphasizes the importance of the time point for distinguishing auto-Ag-specific Tfh clonotypes from potential bystander activated ones.
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Affiliation(s)
- Markus Niebuhr
- Institute for Anatomy, University of Lübeck, Lübeck, Germany
| | - Julia Belde
- Institute for Anatomy, University of Lübeck, Lübeck, Germany
| | - Anke Fähnrich
- Institute for Anatomy, University of Lübeck, Lübeck, Germany.,Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Arnauld Serge
- Laboratoire Adhésion et Inflammation, Inserm U1067 CNRS, Aix-Marseille Université, Marseille, France
| | - Magali Irla
- Centre d'Immunologie de Marseille Luminy (CIML), INSERM U1104, Aix-Marseille Université UM2, Marseille, France
| | - Christoph T Ellebrecht
- Institute for Anatomy, University of Lübeck, Lübeck, Germany.,Department of Dermatology, University of Pennsylvania, Philadelphia, United States
| | - Christoph M Hammers
- Institute for Anatomy, University of Lübeck, Lübeck, Germany.,Department of Dermatology, University of Lübeck, Lübeck, Germany
| | - Katja Bieber
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | | | - Kathrin Kalies
- Institute for Anatomy, University of Lübeck, Lübeck, Germany
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23
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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24
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Yohannes DA, Kaukinen K, Kurppa K, Saavalainen P, Greco D. Clustering based approach for population level identification of condition-associated T-cell receptor β-chain CDR3 sequences. BMC Bioinformatics 2021; 22:159. [PMID: 33765908 PMCID: PMC7993519 DOI: 10.1186/s12859-021-04087-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 03/17/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Deep immune receptor sequencing, RepSeq, provides unprecedented opportunities for identifying and studying condition-associated T-cell clonotypes, represented by T-cell receptor (TCR) CDR3 sequences. However, due to the immense diversity of the immune repertoire, identification of condition relevant TCR CDR3s from total repertoires has mostly been limited to either "public" CDR3 sequences or to comparisons of CDR3 frequencies observed in a single individual. A methodology for the identification of condition-associated TCR CDR3s by direct population level comparison of RepSeq samples is currently lacking. RESULTS We present a method for direct population level comparison of RepSeq samples using immune repertoire sub-units (or sub-repertoires) that are shared across individuals. The method first performs unsupervised clustering of CDR3s within each sample. It then finds matching clusters across samples, called immune sub-repertoires, and performs statistical differential abundance testing at the level of the identified sub-repertoires. It finally ranks CDR3s in differentially abundant sub-repertoires for relevance to the condition. We applied the method on total TCR CDR3β RepSeq datasets of celiac disease patients, as well as on public datasets of yellow fever vaccination. The method successfully identified celiac disease associated CDR3β sequences, as evidenced by considerable agreement of TRBV-gene and positional amino acid usage patterns in the detected CDR3β sequences with previously known CDR3βs specific to gluten in celiac disease. It also successfully recovered significantly high numbers of previously known CDR3β sequences relevant to each condition than would be expected by chance. CONCLUSION We conclude that immune sub-repertoires of similar immuno-genomic features shared across unrelated individuals can serve as viable units of immune repertoire comparison, serving as proxy for identification of condition-associated CDR3s.
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Affiliation(s)
- Dawit A Yohannes
- Research Programs Unit, Translational Immunology, University of Helsinki, Helsinki, Finland.,Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
| | - Katri Kaukinen
- Department of Internal Medicine, Faculty of Medicine and Health Technology, Tampere University Hospital, Tampere University, Tampere, Finland
| | - Kalle Kurppa
- Department of Pediatrics, Tampere University Hospital and Center for Child Health Research, Tampere University, Tampere, Finland
| | - Päivi Saavalainen
- Research Programs Unit, Translational Immunology, University of Helsinki, Helsinki, Finland.,Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. .,BioMediTech Institute, Tampere University, Tampere, Finland. .,Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
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25
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Shemesh O, Polak P, Lundin KEA, Sollid LM, Yaari G. Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls. Front Immunol 2021; 12:627813. [PMID: 33790900 PMCID: PMC8006302 DOI: 10.3389/fimmu.2021.627813] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 02/17/2021] [Indexed: 12/13/2022] Open
Abstract
Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.
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Affiliation(s)
- Or Shemesh
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Pazit Polak
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Knut E. A. Lundin
- K.G. Jebsen Center for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Gastroenterology, Oslo University Hospital Rikshopsitalet, Oslo, Norway
| | - Ludvig M. Sollid
- K.G. Jebsen Center for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Immunology, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Gur Yaari
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
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26
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Ronel T, Harries M, Wicks K, Oakes T, Singleton H, Dearman R, Maxwell G, Chain B. The clonal structure and dynamics of the human T cell response to an organic chemical hapten. eLife 2021; 10:54747. [PMID: 33432924 PMCID: PMC7880692 DOI: 10.7554/elife.54747] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 01/12/2021] [Indexed: 12/27/2022] Open
Abstract
Diphenylcyclopropenone (DPC) is an organic chemical hapten which induces allergic contact dermatitis and is used in the treatment of warts, melanoma, and alopecia areata. This therapeutic setting therefore provided an opportunity to study T cell receptor (TCR) repertoire changes in response to hapten sensitization in humans. Repeated exposure to DPC induced highly dynamic transient expansions of a polyclonal diverse T cell population. The number of TCRs expanded early after sensitization varies between individuals and predicts the magnitude of the allergic reaction. The expanded TCRs show preferential TCR V and J gene usage and consist of clusters of TCRs with similar sequences, two characteristic features of antigen-driven responses. The expanded TCRs share subtle sequence motifs that can be captured using a dynamic Bayesian network. These observations suggest the response to DPC is mediated by a polyclonal population of T cells recognizing a small number of dominant antigens.
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Affiliation(s)
- Tahel Ronel
- Division of Infection and Immunity, University College London, London, United Kingdom.,Cancer Institute, University College London, London, United Kingdom
| | - Matthew Harries
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Salford Royal NHS Foundation Trust (Dermatology Centre), Salford, United Kingdom
| | - Kate Wicks
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Theres Oakes
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Helen Singleton
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Rebecca Dearman
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Gavin Maxwell
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedford, 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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27
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Zhang Z, Xiong D, Wang X, Liu H, Wang T. Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics. Nat Methods 2021; 18:92-99. [PMID: 33408405 PMCID: PMC7799492 DOI: 10.1038/s41592-020-01020-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 11/12/2020] [Indexed: 11/08/2022]
Abstract
Many experimental and bioinformatics approaches have been developed to characterize the human T cell receptor (TCR) repertoire. However, the unknown functional relevance of TCR profiling hinders unbiased interpretation of the biology of T cells. To address this inadequacy, we developed tessa, a tool to integrate TCRs with gene expression of T cells to estimate the effect that TCRs confer on the phenotypes of T cells. Tessa leveraged techniques combining single-cell RNA-sequencing with TCR sequencing. We validated tessa and showed its superiority over existing approaches that investigate only the TCR sequences. With tessa, we demonstrated that TCR similarity constrains the phenotypes of T cells to be similar and dictates a gradient in antigen targeting efficiency of T cell clonotypes with convergent TCRs. We showed this constraint could predict a functional dichotomization of T cells postimmunotherapy treatment and is weakened in tumor contexts.
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Affiliation(s)
- Ze Zhang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Danyi Xiong
- Department of Statistical Science, Southern Methodist University, Dallas, TX, USA
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, Dallas, TX, USA
| | - Hongyu Liu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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28
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Abstract
The adaptive immune system responds to pathogens by selecting clones of cells with specific receptors. While clonal selection in response to particular antigens has been studied in detail, it is unknown how a lifetime of exposures to many antigens collectively shape the immune repertoire. Here, using mathematical modeling and statistical analyses of T cell receptor sequencing data, we develop a quantitative theory of human T cell dynamics compatible with the statistical laws of repertoire organization. We find that clonal expansions during a perinatal time window leave a long-lasting imprint on the human T cell repertoire, which is only slowly reshaped by fluctuating clonal selection during adult life. Our work provides a mechanism for how early clonal dynamics imprint the hierarchy of T cell clone sizes with implications for pathogen defense and autoimmunity.
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Affiliation(s)
- Mario U Gaimann
- Lewis-Sigler Institute for Integrative Genomics, Princeton UniversityPrincetonUnited States
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilians-Universität MünchenMünchenGermany
| | - Maximilian Nguyen
- Lewis-Sigler Institute for Integrative Genomics, Princeton UniversityPrincetonUnited States
| | - Jonathan Desponds
- NSF-Simons Center for Quantitative Biology, Northwestern UniversityEvanstonUnited States
| | - Andreas Mayer
- Lewis-Sigler Institute for Integrative Genomics, Princeton UniversityPrincetonUnited States
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29
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Sethna Z, Isacchini G, Dupic T, Mora T, Walczak AM, Elhanati Y. Population variability in the generation and selection of T-cell repertoires. PLoS Comput Biol 2020; 16:e1008394. [PMID: 33296360 PMCID: PMC7725366 DOI: 10.1371/journal.pcbi.1008394] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/11/2020] [Indexed: 12/21/2022] Open
Abstract
The diversity of T-cell receptor (TCR) repertoires is achieved by a combination of two intrinsically stochastic steps: random receptor generation by VDJ recombination, and selection based on the recognition of random self-peptides presented on the major histocompatibility complex. These processes lead to a large receptor variability within and between individuals. However, the characterization of the variability is hampered by the limited size of the sampled repertoires. We introduce a new software tool SONIA to facilitate inference of individual-specific computational models for the generation and selection of the TCR beta chain (TRB) from sequenced repertoires of 651 individuals, separating and quantifying the variability of the two processes of generation and selection in the population. We find not only that most of the variability is driven by the VDJ generation process, but there is a large degree of consistency between individuals with the inter-individual variance of repertoires being about ∼2% of the intra-individual variance. Known viral-specific TCRs follow the same generation and selection statistics as all TCRs.
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Affiliation(s)
- Zachary Sethna
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Giulio Isacchini
- Laboratoire de physique de l'École Normale Supérieure, PSL University, CNRS, Sorbonne Université, Université de Paris 24 rue Lhomond, Paris, France.,Max Planck Institute for Dynamics and Self-organization, Am Faßberg 17, Göttingen, Germany
| | - Thomas Dupic
- Laboratoire de physique de l'École Normale Supérieure, PSL University, CNRS, Sorbonne Université, Université de Paris 24 rue Lhomond, Paris, France
| | - Thierry Mora
- Laboratoire de physique de l'École Normale Supérieure, PSL University, CNRS, Sorbonne Université, Université de Paris 24 rue Lhomond, Paris, France
| | - Aleksandra M Walczak
- Laboratoire de physique de l'École Normale Supérieure, PSL University, CNRS, Sorbonne Université, Université de Paris 24 rue Lhomond, Paris, France
| | - Yuval Elhanati
- Computational Oncology, Department of Epidemiology and Biostatistics, and Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
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30
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Greiff V, Yaari G, Cowell LG. Mining adaptive immune receptor repertoires for biological and clinical information using machine learning. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.coisb.2020.10.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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31
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Smith NL, Nahrendorf W, Sutherland C, Mooney JP, Thompson J, Spence PJ, Cowan GJM. A Conserved TCRβ Signature Dominates a Highly Polyclonal T-Cell Expansion During the Acute Phase of a Murine Malaria Infection. Front Immunol 2020; 11:587756. [PMID: 33329568 PMCID: PMC7719809 DOI: 10.3389/fimmu.2020.587756] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/27/2020] [Indexed: 01/31/2023] Open
Abstract
CD4+ αβ T-cells are key mediators of the immune response to a first Plasmodium infection, undergoing extensive activation and splenic expansion during the acute phase of an infection. However, the clonality and clonal composition of this expansion has not previously been described. Using a comparative infection model, we sequenced the splenic CD4+ T-cell receptor repertoires generated over the time-course of a Plasmodium chabaudi infection. We show through repeat replicate experiments, single-cell RNA-seq, and analyses of independent RNA-seq data, that following a first infection - within a highly polyclonal expansion - T-effector repertoires are consistently dominated by TRBV3 gene usage. Clustering by sequence similarity, we find the same dominant clonal signature is expanded across replicates in the acute phase of an infection, revealing a conserved pathogen-specific T-cell response that is consistently a hallmark of a first infection, but not expanded upon re-challenge. Determining the host or parasite factors driving this conserved response may uncover novel immune targets for malaria therapeutic purposes.
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Affiliation(s)
- Natasha L. Smith
- Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
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32
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Lee CH, Salio M, Napolitani G, Ogg G, Simmons A, Koohy H. Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors. Front Immunol 2020; 11:565096. [PMID: 33193332 PMCID: PMC7642207 DOI: 10.3389/fimmu.2020.565096] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 09/07/2020] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- Chloe H. Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Mariolina Salio
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Giorgio Napolitani
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Graham Ogg
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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33
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Vujovic M, Degn KF, Marin FI, Schaap-Johansen AL, Chain B, Andresen TL, Kaplinsky J, Marcatili P. T cell receptor sequence clustering and antigen specificity. Comput Struct Biotechnol J 2020; 18:2166-2173. [PMID: 32952933 PMCID: PMC7473833 DOI: 10.1016/j.csbj.2020.06.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/25/2020] [Accepted: 06/27/2020] [Indexed: 11/17/2022] Open
Abstract
There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.
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Affiliation(s)
- Milena Vujovic
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Kristine Fredlund Degn
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Frederikke Isa Marin
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Anna-Lisa Schaap-Johansen
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Benny Chain
- UCL Division of Infection and Immunity, University College London, Wing 3.2, Cruciform Building, Gower Street, London WC1E 6BT, United Kingdom
| | - Thomas Lars Andresen
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Joseph Kaplinsky
- Ludwig Institute for Cancer Research Ltd, University of Oxford, Nuffield Department of Medicine, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Paolo Marcatili
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
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34
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Teraguchi S, Saputri DS, Llamas-Covarrubias MA, Davila A, Diez D, Nazlica SA, Rozewicki J, Ismanto HS, Wilamowski J, Xie J, Xu Z, Loza-Lopez MDJ, van Eerden FJ, Li S, Standley DM. Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput Struct Biotechnol J 2020; 18:2000-2011. [PMID: 32802272 PMCID: PMC7366105 DOI: 10.1016/j.csbj.2020.07.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/08/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.
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Affiliation(s)
- Shunsuke Teraguchi
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Dianita S. Saputri
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Mara Anais Llamas-Covarrubias
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Mexico
| | - Ana Davila
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Diego Diez
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Sedat Aybars Nazlica
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - John Rozewicki
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Hendra S. Ismanto
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jiaqi Xie
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | | | - Floris J. van Eerden
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Daron M. Standley
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
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35
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Meysman P, De Neuter N, Gielis S, Bui Thi D, Ogunjimi B, Laukens K. On the viability of unsupervised T-cell receptor sequence clustering for epitope preference. Bioinformatics 2020; 35:1461-1468. [PMID: 30247624 DOI: 10.1093/bioinformatics/bty821] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 08/29/2018] [Accepted: 09/20/2018] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering. RESULTS We provide an overview of the performance of various distance metrics on two large independent datasets with 412 and 2835 TCR sequences respectively. Our results confirm the presence of structural distinct TCR groups that target identical epitopes. In addition, we put forward several recommendations to perform unsupervised T-cell receptor sequence clustering. AVAILABILITY AND IMPLEMENTATION Source code implemented in Python 3 available at https://github.com/pmeysman/TCRclusteringPaper. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pieter Meysman
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).,Department of Computer Science and Mathematics, ADREM Data Lab.,Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium
| | - Nicolas De Neuter
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).,Department of Computer Science and Mathematics, ADREM Data Lab.,Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium
| | - Sofie Gielis
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).,Department of Computer Science and Mathematics, ADREM Data Lab.,Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium
| | - Danh Bui Thi
- Department of Computer Science and Mathematics, ADREM Data Lab.,Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium
| | - Benson Ogunjimi
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).,Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine & Infectious Disease Institute (VAXINFECTIO).,Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Wilrijk, Belgium.,Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium
| | - Kris Laukens
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).,Department of Computer Science and Mathematics, ADREM Data Lab.,Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium
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36
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Cowell LG. The Diagnostic, Prognostic, and Therapeutic Potential of Adaptive Immune Receptor Repertoire Profiling in Cancer. Cancer Res 2019; 80:643-654. [PMID: 31888887 DOI: 10.1158/0008-5472.can-19-1457] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 10/14/2019] [Accepted: 12/17/2019] [Indexed: 11/16/2022]
Abstract
Lymphocytes play a critical role in antitumor immune responses. They are directly targeted by some therapies, and the composition and spatial organization of intratumor T-cell populations is prognostic in some cancer types. A better understanding of lymphocyte population dynamics over the course of disease and in response to therapy is urgently needed to guide therapy decisions and to develop new therapy targets. Deep sequencing of the repertoire of antigen receptor-encoding genes expressed in a lymphocyte population has become a widely used approach for profiling the population's immune status. Lymphocyte antigen receptor repertoire deep sequencing data can be used to assess the clonal richness and diversity of lymphocyte populations; to track clone members over time, between tissues, and across lymphocyte subsets; to detect clonal expansion; and to detect the recruitment of new clones into a tissue. Repertoire sequencing is thus a critical complement to other methods of lymphocyte and immune profiling in cancer. This review describes the current state of knowledge based on repertoire sequencing studies conducted on human cancer patients, with a focus on studies of the T-cell receptor beta chain locus. The review then outlines important questions left unanswered and suggests future directions for the field.
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Affiliation(s)
- Lindsay G Cowell
- Department of Population and Data Sciences, Department of Immunology, UT Southwestern Medical Center, Dallas, Texas.
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37
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Wolf K, Hether T, Gilchuk P, Kumar A, Rajeh A, Schiebout C, Maybruck J, Buller RM, Ahn TH, Joyce S, DiPaolo RJ. Identifying and Tracking Low-Frequency Virus-Specific TCR Clonotypes Using High-Throughput Sequencing. Cell Rep 2019; 25:2369-2378.e4. [PMID: 30485806 PMCID: PMC7770954 DOI: 10.1016/j.celrep.2018.11.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 09/18/2018] [Accepted: 10/31/2018] [Indexed: 12/30/2022] Open
Abstract
Tracking antigen-specific T cell responses over time within individuals is difficult because of lack of knowledge of antigen-specific TCR sequences, limitations in sample size, and assay sensitivities. We hypothesized that analyses of high-throughput sequencing of TCR clonotypes could provide functional readouts of individuals' immunological histories. Using high-throughput TCR sequencing, we develop a database of TCRβ sequences from large cohorts of mice before (naive) and after smallpox vaccination. We computationally identify 315 vaccine-associated TCR sequences (VATS) that are used to train a diagnostic classifier that distinguishes naive from vaccinated samples in mice up to 9 months post-vaccination with >99% accuracy. We determine that the VATS library contains virus-responsive TCRs by in vitro expansion assays and virus-specific tetramer sorting. These data outline a platform for advancing our capabilities to identify pathogen-specific TCR sequences, which can be used to identify and quantitate low-frequency pathogen-specific TCR sequences in circulation over time with exceptional sensitivity.
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Affiliation(s)
- Kyle Wolf
- Department of Molecular Microbiology and Immunology, Saint Louis University, Saint Louis, MO 63104, USA
| | - Tyler Hether
- Adaptive Biotechnologies, Seattle, WA 98102, USA
| | - Pavlo Gilchuk
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN 37212, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN 37232, USA
| | - Amrendra Kumar
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN 37212, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN 37232, USA
| | - Ahmad Rajeh
- Program in Bioinformatics and Computational Biology, Saint Louis University, Saint Louis, MO 63104, USA
| | - Courtney Schiebout
- Program in Bioinformatics and Computational Biology, Saint Louis University, Saint Louis, MO 63104, USA
| | - Julie Maybruck
- Federal Bureau of Investigation, Washington, DC 20535, USA
| | - R Mark Buller
- Department of Molecular Microbiology and Immunology, Saint Louis University, Saint Louis, MO 63104, USA
| | - Tae-Hyuk Ahn
- Department of Computer Science, Saint Louis University, Saint Louis, MO 63104, USA; Program in Bioinformatics and Computational Biology, Saint Louis University, Saint Louis, MO 63104, USA
| | - Sebastian Joyce
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN 37212, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN 37232, USA
| | - Richard J DiPaolo
- Department of Molecular Microbiology and Immunology, Saint Louis University, Saint Louis, MO 63104, USA.
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38
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Olson BJ, Moghimi P, Schramm CA, Obraztsova A, Ralph D, Vander Heiden JA, Shugay M, Shepherd AJ, Lees W, Matsen FA. sumrep: A Summary Statistic Framework for Immune Receptor Repertoire Comparison and Model Validation. Front Immunol 2019; 10:2533. [PMID: 31736960 PMCID: PMC6838214 DOI: 10.3389/fimmu.2019.02533] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 10/11/2019] [Indexed: 12/28/2022] Open
Abstract
The adaptive immune system generates an incredible diversity of antigen receptors for B and T cells to keep dangerous pathogens at bay. The DNA sequences coding for these receptors arise by a complex recombination process followed by a series of productivity-based filters, as well as affinity maturation for B cells, giving considerable diversity to the circulating pool of receptor sequences. Although these datasets hold considerable promise for medical and public health applications, the complex structure of the resulting adaptive immune receptor repertoire sequencing (AIRR-seq) datasets makes analysis difficult. In this paper we introduce sumrep, an R package that efficiently performs a wide variety of repertoire summaries and comparisons, and show how sumrep can be used to perform model validation. We find that summaries vary in their ability to differentiate between datasets, although many are able to distinguish between covariates such as donor, timepoint, and cell type for BCR and TCR repertoires. We show that deletion and insertion lengths resulting from V(D)J recombination tend to be more discriminative characterizations of a repertoire than summaries that describe the amino acid composition of the CDR3 region. We also find that state-of-the-art generative models excel at recapitulating gene usage and recombination statistics in a given experimental repertoire, but struggle to capture many physiochemical properties of real repertoires.
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Affiliation(s)
- Branden J Olson
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States.,Department of Statistics, University of Washington, Seattle, WA, United States
| | - Pejvak Moghimi
- Department of Biological Sciences, Institute of Structural and Molecular Biology, Birkbeck, University of London, London, United Kingdom
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Anna Obraztsova
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.,Genomics of Adaptive Immunity Department, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Duncan Ralph
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Jason A Vander Heiden
- Department of Bioinformatics and Computational Biology, Genentech, Inc., South San Francisco, CA, United States
| | - Mikhail Shugay
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.,Genomics of Adaptive Immunity Department, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,Department of Molecular Technologies, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Adrian J Shepherd
- Department of Biological Sciences, Institute of Structural and Molecular Biology, Birkbeck, University of London, London, United Kingdom
| | - William Lees
- Department of Biological Sciences, Institute of Structural and Molecular Biology, Birkbeck, University of London, London, United Kingdom
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39
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Abstract
Immune repertoire is a collection of enormously diverse adaptive immune cells within an individual. As the repertoire shapes and represents immunological conditions, identification of clones and characterization of diversity are critical for understanding how to protect ourselves against various illness such as infectious diseases and cancers. Over the past several years, fast growing technologies for high throughput sequencing have facilitated rapid advancement of repertoire research, enabling us to observe the diversity of repertoire at an unprecedented level. Here, we focus on B cell receptor (BCR) repertoire and review approaches to B cell isolation and sequencing library construction. These experiments should be carefully designed according to BCR regions to be interrogated, such as heavy chain full length, complementarity determining regions, and isotypes. We also highlight preprocessing steps to remove sequencing and PCR errors with unique molecular index and bioinformatics techniques. Due to the nature of massive sequence variation in BCR, caution is warranted when interpreting repertoire diversity from error-prone sequencing data. Furthermore, we provide a summary of statistical frameworks and bioinformatics tools for clonal evolution and diversity. Finally, we discuss limitations of current BCR-seq technologies and future perspectives on advances in repertoire sequencing.
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Affiliation(s)
- Daeun Kim
- Department of Biological Sciences, College of Natural Sciences, Ajou University, Suwon 16499, Korea
| | - Daechan Park
- Department of Biological Sciences, College of Natural Sciences, Ajou University, Suwon 16499, Korea
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40
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Davis MM, Boyd SD. Recent progress in the analysis of αβT cell and B cell receptor repertoires. Curr Opin Immunol 2019; 59:109-114. [PMID: 31326777 DOI: 10.1016/j.coi.2019.05.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 05/28/2019] [Indexed: 01/10/2023]
Abstract
T cell receptors (TCRs) and B cell receptors (BCRs) are vertebrate evolution's best answer to the threat of microbial pathogens that can evolve much faster than ourselves. These antigen receptors are generated during T cell or B cell development by combinatorial rearrangement of germline genome V, D and J gene segments, and with junctional residues capable of enormous diversity. For decades the complexity of these receptor repertoires has limited their analysis, but advances in DNA sequencing technology and an array of complementary tools have now made their study much more tractable, filling a major gap in our ability to understand immunology as a system. Here, we summarize the recent approaches and discoveries that are enabling these advances, with some suggestions as to what may lie ahead.
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Affiliation(s)
- Mark M Davis
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA; The Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| | - Scott D Boyd
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA; The Sean N. Parker Center for Allergy and Asthma Research at Stanford University, Stanford, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
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41
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Konishi H, Komura D, Katoh H, Atsumi S, Koda H, Yamamoto A, Seto Y, Fukayama M, Yamaguchi R, Imoto S, Ishikawa S. Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning. BMC Bioinformatics 2019; 20:267. [PMID: 31138102 PMCID: PMC6537402 DOI: 10.1186/s12859-019-2853-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 04/26/2019] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs. RESULTS RNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric cancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or tumor classes. Different machine learning models using various features were constructed as well as gradient boosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences between normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify individual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an area under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct sequence-level features in normal and tumor tissues. CONCLUSIONS To the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor and normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig repertoires.
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Affiliation(s)
- Hiroki Konishi
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
- Institute of Medical Science, Health Intelligence Center, The University of Tokyo, Tokyo, 108-8639 Japan
| | - Daisuke Komura
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Hiroto Katoh
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Shinichiro Atsumi
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
- Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Hirotomo Koda
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Asami Yamamoto
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Yasuyuki Seto
- Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Masashi Fukayama
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Rui Yamaguchi
- Institute of Medical Science, Human Genome Center, The University of Tokyo, Tokyo, 108-8639 Japan
| | - Seiya Imoto
- Institute of Medical Science, Health Intelligence Center, The University of Tokyo, Tokyo, 108-8639 Japan
| | - Shumpei Ishikawa
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
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42
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Kuehm LM, Wolf K, Zahour J, DiPaolo RJ, Teague RM. Checkpoint blockade immunotherapy enhances the frequency and effector function of murine tumor-infiltrating T cells but does not alter TCRβ diversity. Cancer Immunol Immunother 2019; 68:1095-106. [PMID: 31104075 DOI: 10.1007/s00262-019-02346-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 05/11/2019] [Indexed: 01/29/2023]
Abstract
Checkpoint blockade immunotherapy is now a first-line treatment option for patients with melanoma. Despite achieving objective responses in about half of patients, the exact immune mechanisms elicited and those required for therapeutic success have not been clearly identified. Insight into these mechanisms is key for improving outcomes in a broader range of cancer patients. We used a murine melanoma model to track responses by different subsets of tumor-infiltrating lymphocytes (TIL) during checkpoint blockade immunotherapy. Tumors from treated mice had increased frequencies of both CD4+ and CD8+ T cells, which also showed evidence of functional reinvigoration and elevated effector cytokine production after immunotherapy. We predicted that increased T cell numbers and function within tumors reflected either infiltration by new T cells or clonal expansion by a few high-affinity tumor-reactive T cells. To address this, we compared TIL diversity before and after immunotherapy by sequencing the complementarity determining region 3 (CDR3) of all T cell receptor beta (TCRβ) genes. While checkpoint blockade effectively slowed tumor progression and increased T cell frequencies, the diversity of intratumoral T cells remained stable. This was true when analyzing total T cells and when focusing on smaller subsets of effector CD4+ and CD8+ TIL as well as regulatory T cells. Our study suggests that checkpoint blockade immunotherapy does not broaden the T cell repertoire within murine melanoma tumors, but rather expands existing T cell populations and enhances effector capabilities.
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43
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Abstract
Adaptive immune recognition is mediated by antigen receptors on B and T cells generated by somatic recombination during lineage development. The high level of diversity resulting from this process posed technical limitations that previously limited the comprehensive analysis of adaptive immune recognition. Advances over the last ten years have produced data and approaches allowing insights into how T cells develop, evolutionary signatures of recombination and selection, and the features of T cell receptors that mediate epitope-specific binding and T cell activation. The size and complexity of these data have necessitated the generation of novel computational and analytical approaches, which are transforming how T cell immunology is conducted. Here we review the development and application of novel biological, theoretical, and computational methods for understanding T cell recognition and discuss the potential for improved models of receptor:antigen interactions.
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Affiliation(s)
- Philip Bradley
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA; .,Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Paul G Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA;
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44
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Ostmeyer J, Christley S, Toby IT, Cowell LG. Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue. Cancer Res 2019; 79:1671-1680. [PMID: 30622114 DOI: 10.1158/0008-5472.can-18-2292] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/16/2018] [Accepted: 01/03/2019] [Indexed: 12/19/2022]
Abstract
Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.See related commentary by Zoete and Coukos, p. 1299.
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Affiliation(s)
- Jared Ostmeyer
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas
| | - Scott Christley
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas
| | - Inimary T Toby
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas
| | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.
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45
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Abstract
T cells play a crucial role in the immune system's defense against many infectious diseases, including persistent infections for which no effective vaccines currently exist. The T cell component of the adaptive immune system is highly complex involving a constantly evolving landscape of various inter-related T cell populations. These T cell populations are characterized by their phenotypic and functional properties as well as the collection, or repertoire, of T cell receptors (TCR) that mediate T cell recognition of antigenic peptides derived from pathogens. Understanding the various processes and factors that impact the development and evolution of the broader T cell repertoire available to recognize and respond to pathogens and the characteristics of antigen-experienced T cell repertoires associated with effective immune control of pathogens is critical to the rational design of T cell-based vaccines and therapies. In this article we discuss, using examples of recent research, the promise that systems immunology approaches, involving quantitative analysis and mathematical and computational modeling of immunological data, hold for decoding the complex TCR repertoire system in the current era of advancing technologies.
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Affiliation(s)
- Vanessa Venturi
- Infection Analytics Program, Kirby Institute for Infection and Immunity, UNSW Australia, Sydney, NSW, Australia
| | - Paul G Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
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46
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DeWitt WS, Smith A, Schoch G, Hansen JA, Matsen FA, Bradley P. Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity. eLife 2018; 7:e38358. [PMID: 30152754 PMCID: PMC6162092 DOI: 10.7554/elife.38358] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 08/21/2018] [Indexed: 12/24/2022] Open
Abstract
The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity.
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Affiliation(s)
- William S DeWitt
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome SciencesUniversity of WashingtonSeattleUnited States
| | - Anajane Smith
- Clinical DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Gary Schoch
- Clinical DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
| | - John A Hansen
- Clinical DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
- Department of MedicineUniversity of WashingtonSeattleUnited States
| | - Frederick A Matsen
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome SciencesUniversity of WashingtonSeattleUnited States
| | - Philip Bradley
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
- Institute for Protein DesignUniversity of WashingtonSeattleUnited States
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47
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Heather JM, Ismail M, Oakes T, Chain B. High-throughput sequencing of the T-cell receptor repertoire: pitfalls and opportunities. Brief Bioinform 2018; 19:554-565. [PMID: 28077404 PMCID: PMC6054146 DOI: 10.1093/bib/bbw138] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 11/21/2016] [Indexed: 02/06/2023] Open
Abstract
T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information. The latest generation of bioinformatics tools allows millions of DNA sequences to be accurately and rapidly assigned to their respective variable V and J gene segments, and to reconstruct an almost error-free representation of the non-templated additions and deletions that occur. High-level processing can measure the diversity of the repertoire in different samples, quantify V and J usage and identify private and public T-cell receptors. Finally, we discuss the major challenge of linking T-cell receptor sequence to function, and specifically to antigen recognition. Sophisticated machine learning algorithms are being developed that can combine the paradoxical degeneracy and cross-reactivity of individual T-cell receptors with the specificity of the overall T-cell immune response. Computational analysis will provide the key to unlock the potential of the T-cell receptor repertoire to give insight into the fundamental biology of the adaptive immune system and to provide powerful biomarkers of disease.
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Affiliation(s)
| | | | | | - Benny Chain
- Division of Infection and Immunity, University College of London, Bloomsbury, UK
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48
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Abstract
Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given datasets. This procedure is well developed in the Bayesian perspective, in which one infers probability distributions describing to what extent various possible parameters agree with the data. In this paper, we motivate and review probabilistic modeling for adaptive immune receptor repertoire data then describe progress and prospects for future work, from germline haplotyping to adaptive immune system deployment across tissues. The relevant quantities in immune sequence analysis include not only continuous parameters such as gene use frequency but also discrete objects such as B-cell clusters and lineages. Throughout this review, we unravel the many opportunities for probabilistic modeling in adaptive immune receptor analysis, including settings for which the Bayesian approach holds substantial promise (especially if one is optimistic about new computational methods). From our perspective, the greatest prospects for progress in probabilistic modeling for repertoires concern ancestral sequence estimation for B-cell receptor lineages, including uncertainty from germline genotype, rearrangement, and lineage development.
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Affiliation(s)
- Branden Olson
- Computational Biology Program Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Mail stop: M1-B514 Seattle, WA 98109-1024 phone: +1 206 667 7318
| | - Frederick A. Matsen
- Computational Biology Program Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Mail stop: M1-B514 Seattle, WA 98109-1024 phone: +1 206 667 7318
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49
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Fähnrich A, Klein S, Sergé A, Nyhoegen C, Kombrink S, Möller S, Keller K, Westermann J, Kalies K. CD154 Costimulation Shifts the Local T-Cell Receptor Repertoire Not Only During Thymic Selection but Also During Peripheral T-Dependent Humoral Immune Responses. Front Immunol 2018; 9:1019. [PMID: 29867987 PMCID: PMC5966529 DOI: 10.3389/fimmu.2018.01019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 04/24/2018] [Indexed: 12/20/2022] Open
Abstract
CD154 is a transmembrane cytokine expressed transiently on activated CD4 T cells upon T-cell receptor (TCR) stimulation that interacts with CD40 on antigen-presenting cells. The signaling via CD154:CD40 is essential for B-cell maturation and germinal center formation and also for the final differentiation of CD4 T cells during T-dependent humoral immune responses. Recent data demonstrate that CD154 is critically involved in the selection of T-cell clones during the negative selection process in the thymus. Whether CD154 signaling influences the TCR repertoire during peripheral T-dependent humoral immune responses has not yet been elucidated. To find out, we used CD154-deficient mice and assessed the global TCRβ repertoire in T-cell zones (TCZ) of spleens by high-throughput sequencing after induction of a Th2 response to the multiepitopic antigen sheep red blood cells. Qualitative and quantitative comparison of the splenic TCZ-specific TCRβ repertoires revealed that CD154 deficiency shifts the distribution of Vβ-Jβ genes after antigen exposure. This data led to the conclusion that costimulation via CD154:CD40 during the interaction of T cells with CD40-matured B cells contributes to the recruitment of T-cell clones into the immune response and thereby shapes the peripheral TCR repertoire.
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Affiliation(s)
- Anke Fähnrich
- Institute of Anatomy, University of Luebeck, Luebeck, Germany
| | - Sebastian Klein
- Institute of Anatomy, University of Luebeck, Luebeck, Germany
| | - Arnauld Sergé
- Centre de Recherche en Cancérologie de Marseille (CRCM) U1068 INSERM - UMR7258 CNRS - Institut Paoli Calmette, Aix-Marseille University, UM105, Marseille, France
| | | | - Sabrina Kombrink
- Institute of Mathematics, University of Luebeck, Luebeck, Germany
| | - Steffen Möller
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock, Germany
| | - Karsten Keller
- Institute of Mathematics, University of Luebeck, Luebeck, Germany
| | | | - Kathrin Kalies
- Institute of Anatomy, University of Luebeck, Luebeck, Germany
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50
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Chaara W, Gonzalez-Tort A, Florez LM, Klatzmann D, Mariotti-Ferrandiz E, Six A. RepSeq Data Representativeness and Robustness Assessment by Shannon Entropy. Front Immunol 2018; 9:1038. [PMID: 29868003 PMCID: PMC5962720 DOI: 10.3389/fimmu.2018.01038] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 04/25/2018] [Indexed: 12/30/2022] Open
Abstract
High-throughput sequencing (HTS) has the potential to decipher the diversity of T cell repertoires and their dynamics during immune responses. Applied to T cell subsets such as T effector and T regulatory cells, it should help identify novel biomarkers of diseases. However, given the extreme diversity of TCR repertoires, understanding how the sequencing conditions, including cell numbers, biological and technical sampling and sequencing depth, impact the experimental outcome is critical to proper use of these data. Here, we assessed the representativeness and robustness of TCR repertoire diversity assessment according to experimental conditions. By comparative analyses of experimental datasets and computer simulations, we found that (i) for small samples, the number of clonotypes recovered is often higher than the number of cells per sample, even after removing the singletons; (ii) high-sequencing depth for small samples alters the clonotype distributions, which can be corrected by filtering the datasets using Shannon entropy as a threshold; and (iii) a single sequencing run at high depth does not ensure a good coverage of the clonotype richness in highly polyclonal populations, which can be better covered using multiple sequencing. Altogether, our results warrant better understanding and awareness of the limitation of TCR diversity analyses by HTS and justify the development of novel computational tools for improved modeling of the highly complex nature of TCR repertoires.
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Affiliation(s)
- Wahiba Chaara
- Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France.,AP-HP, Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi) and Inflammation-Immunopathology-Biotherapy Department (i2B), Paris, France
| | - Ariadna Gonzalez-Tort
- Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France
| | - Laura-Maria Florez
- Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France
| | - David Klatzmann
- Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France.,AP-HP, Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi) and Inflammation-Immunopathology-Biotherapy Department (i2B), Paris, France
| | - Encarnita Mariotti-Ferrandiz
- Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France.,AP-HP, Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi) and Inflammation-Immunopathology-Biotherapy Department (i2B), Paris, France
| | - Adrien Six
- Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France.,AP-HP, Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi) and Inflammation-Immunopathology-Biotherapy Department (i2B), Paris, France
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