1
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Yin R, Melton S, Huseby ES, Kardar M, Chakraborty AK. How persistent infection overcomes peripheral tolerance mechanisms to cause T cell-mediated autoimmune disease. Proc Natl Acad Sci U S A 2024; 121:e2318599121. [PMID: 38446856 PMCID: PMC10945823 DOI: 10.1073/pnas.2318599121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/06/2024] [Indexed: 03/08/2024] Open
Abstract
T cells help orchestrate immune responses to pathogens, and their aberrant regulation can trigger autoimmunity. Recent studies highlight that a threshold number of T cells (a quorum) must be activated in a tissue to mount a functional immune response. These collective effects allow the T cell repertoire to respond to pathogens while suppressing autoimmunity due to circulating autoreactive T cells. Our computational studies show that increasing numbers of pathogenic peptides targeted by T cells during persistent or severe viral infections increase the probability of activating T cells that are weakly reactive to self-antigens (molecular mimicry). These T cells are easily re-activated by the self-antigens and contribute to exceeding the quorum threshold required to mount autoimmune responses. Rare peptides that activate many T cells are sampled more readily during severe/persistent infections than in acute infections, which amplifies these effects. Experiments in mice to test predictions from these mechanistic insights are suggested.
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Affiliation(s)
- Rose Yin
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Samuel Melton
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Eric S. Huseby
- Basic Pathology, Department of Pathology, University of Massachusetts Medical School, Worcester, MA01655
| | - Mehran Kardar
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Arup K. Chakraborty
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA02139
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA02139
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA02139
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2
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Ghoreyshi ZS, George JT. Quantitative approaches for decoding the specificity of the human T cell repertoire. Front Immunol 2023; 14:1228873. [PMID: 37781387 PMCID: PMC10539903 DOI: 10.3389/fimmu.2023.1228873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023] Open
Abstract
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.
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Affiliation(s)
- Zahra S. Ghoreyshi
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Jason T. George
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Engineering Medicine Program, Texas A&M University, Houston, TX, United States
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
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3
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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] [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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4
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Ng Chau K, George JT, Onuchic JN, Lin X, Levine H. Contact map dependence of a T-cell receptor binding repertoire. Phys Rev E 2022; 106:014406. [PMID: 35974642 DOI: 10.1103/physreve.106.014406] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
The T-cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T-cell receptor (TCR) specific to non-self-antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T-cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.
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Affiliation(s)
- Kevin Ng Chau
- Physics Department, Northeastern University, Boston, Massachusetts 02115, USA
| | - Jason T George
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA
| | - José N Onuchic
- Center for Theoretical Biological Physics and Departments of Physics and Astronomy, Chemistry and Biosciences, Rice University, Houston, Texas 77005, USA
| | - Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics and Departments of Physics and Bioengineering, Northeastern University, Boston, Massachusetts 02115, USA
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5
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Tregs self-organize into a computing ecosystem and implement a sophisticated optimization algorithm for mediating immune response. Proc Natl Acad Sci U S A 2021; 118:2011709118. [PMID: 33372155 DOI: 10.1073/pnas.2011709118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Regulatory T cells (Tregs) play a crucial role in mediating immune response. Yet an algorithmic understanding of the role of Tregs in adaptive immunity remains lacking. Here, we present a biophysically realistic model of Treg-mediated self-tolerance in which Tregs bind to self-antigens and locally inhibit the proliferation of nearby activated T cells. By exploiting a duality between ecological dynamics and constrained optimization, we show that Tregs tile the potential antigen space while simultaneously minimizing the overlap between Treg activation profiles. We find that for sufficiently high Treg diversity, Treg-mediated self-tolerance is robust to fluctuations in self-antigen concentrations but lowering the Treg diversity results in a sharp transition-related to the Gardner transition in perceptrons-to a regime where changes in self-antigen concentrations can result in an autoimmune response. We propose an experimental test of this transition in immune-deficient mice and discuss potential implications for autoimmune diseases.
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6
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Gao A, Chen Z, Amitai A, Doelger J, Mallajosyula V, Sundquist E, Pereyra Segal F, Carrington M, Davis MM, Streeck H, Chakraborty AK, Julg B. Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2. iScience 2021; 24:102311. [PMID: 33748696 PMCID: PMC7956900 DOI: 10.1016/j.isci.2021.102311] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/22/2021] [Accepted: 03/10/2021] [Indexed: 12/18/2022] Open
Abstract
We describe a physics-based learning model for predicting the immunogenicity of cytotoxic T lymphocyte (CTL) epitopes derived from diverse pathogens including SARS-CoV-2. The model was trained and optimized on the relative immunodominance of CTL epitopes in human immunodeficiency virus infection. Its accuracy was tested against experimental data from patients with COVID-19. Our model predicts that only some SARS-CoV-2 epitopes predicted to bind to HLA molecules are immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but those from the SARS-CoV-2 spike protein alone are unlikely to do so. Our model also predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to seasonal coronaviruses circulating in the population and such cross-reactive CD8+ T cells can indeed be detected in prepandemic blood donors, suggesting that some level of CTL immunity against COVID-19 may be present in some individuals before SARS-CoV-2 infection.
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Affiliation(s)
- Ang Gao
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Zhilin Chen
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA
| | - Assaf Amitai
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Julia Doelger
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Vamsee Mallajosyula
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Emily Sundquist
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA
| | | | - Mary Carrington
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Mark M. Davis
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hendrik Streeck
- Institut für Virologie, Universitätsklinikum Bonn, 53127 Bonn, Germany
| | - Arup K. Chakraborty
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Boris Julg
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA
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7
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Sohail MS, Ahmed SF, Quadeer AA, McKay MR. In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives. Adv Drug Deliv Rev 2021; 171:29-47. [PMID: 33465451 PMCID: PMC7832442 DOI: 10.1016/j.addr.2021.01.007] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/31/2020] [Accepted: 01/07/2021] [Indexed: 02/06/2023]
Abstract
Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.
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Affiliation(s)
- Muhammad Saqib Sohail
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Syed Faraz Ahmed
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Matthew R McKay
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
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8
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Gao A, Chen Z, Segal FP, Carrington M, Streeck H, Chakraborty AK, Julg B. Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.05.14.095885. [PMID: 32511339 PMCID: PMC7241102 DOI: 10.1101/2020.05.14.095885] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic T Lymphocyte (CTL) epitopes derived from diverse pathogens, given a Human Leukocyte Antigen (HLA) genotype. The model was trained and tested on experimental data on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. The method is more accurate than publicly available models. Our model predicts that only a fraction of SARS-CoV-2 epitopes that have been predicted to bind to HLA molecules is immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but the immunogenic epitopes in the SARS-CoV-2 spike protein alone are unlikely to do so. Our model predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to those contained in low-pathogenicity coronaviruses circulating in the population. Thus, we suggest that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.
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Affiliation(s)
- Ang Gao
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139, USA
| | - Zhilin Chen
- Ragon Insitute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, USA
| | | | - Mary Carrington
- Ragon Insitute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, USA
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Hendrik Streeck
- Institut für Virologie, Universitätsklinikum Bonn, 53127 Bonn, Germany
| | - Arup K. Chakraborty
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Ragon Insitute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139, USA
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Boris Julg
- Ragon Insitute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, USA
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9
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Xu J, Jo J. Broad cross-reactivity of the T-cell repertoire achieves specific and sufficiently rapid target searching. J Theor Biol 2019; 466:119-127. [PMID: 30699327 DOI: 10.1016/j.jtbi.2019.01.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 12/20/2018] [Accepted: 01/24/2019] [Indexed: 11/29/2022]
Abstract
The molecular recognition of T-cell receptors is the hallmark of the adaptive immunity. Given the finiteness of the T-cell repertoire, individual T-cell receptors are necessary to be cross-reactive to multiple antigenic peptides. In this study, we quantify the variability of the cross-reactivity by using a string model that estimates the binding affinity between two sequences of amino acids. We examine sequences of 10,000 human T-cell receptors and 10,000 antigenic peptides, and obtain a full spectrum of cross-reactivity of the receptor-peptide binding. Then, we find that the cross-reactivity spectrum is broad. Some T-cells are reactive to 1000 peptides, but some T-cells are reactive to only one or two peptides. Since the degree of cross-reactivity has a correlation with the (un)binding affinity of receptors, we further investigate how the broad cross-reactivity affects the target searching of T-cells. High cross-reactive T-cells may not require many trials for searching correct targets, but they may spend long time to unbind from incorrect targets. In contrast, low cross-reactive T-cells may not spend long time to ignore incorrect targets, but they require many trials for screening correct targets. We evaluate this hypothesis, and show that the broad cross-reactivity of the natural T-cell repertoire can balance the trade-off between the rapid screening and unbinding penalty.
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Affiliation(s)
- Jin Xu
- Asia Pacific Center for Theoretical Physics (APCTP), 67 Cheongam-ro, Pohang, 37673, South Korea; Department of Physics, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang, 37673, South Korea
| | - Junghyo Jo
- Asia Pacific Center for Theoretical Physics (APCTP), 67 Cheongam-ro, Pohang, 37673, South Korea; Department of Physics, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Pohang, 37673, South Korea; School of Computational Sciences, Korea Institute for Advanced Study (KIAS), 85 Hoegiro, Seoul, 02455, South Korea; Department of Statistics, Keimyung University, 1095 Dalgubeol-daero, Daegu, 42601, South Korea.
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10
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Banerjee S, Chapman SJ. Influence of correlated antigen presentation on T-cell negative selection in the thymus. J R Soc Interface 2018; 15:rsif.2018.0311. [PMID: 30404905 PMCID: PMC6283997 DOI: 10.1098/rsif.2018.0311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 10/08/2018] [Indexed: 11/12/2022] Open
Abstract
The thymus is the primary organ for the generation of naive T cells, a key component of the immune system. Tolerance of T cells to self is achieved primarily in the thymic medulla, where immature T cells (thymocytes) sample self-peptides presented by medullary thymic epithelial cells (mTECs). A sufficiently strong interaction activates the thymocytes leading to negative selection. A key question of current interest is whether there is any structure in the manner in which mTECs present peptides: can any mTEC present any peptide at any time, or are there particular patterns of correlated peptide presentation? We investigate this question using a mathematical model of negative selection. We find that correlated patterns of peptide presentation may be advantageous in negatively selecting low-degeneracy thymocytes (that is, those thymocytes which respond to relatively few peptides). We also quantify the probability that an auto-reactive thymocyte exits the thymus before it encounters a cognate antigen. The results suggest that heterogeneity of gene co-expression in mTECs has an effect on the probability of escape of autoreactive thymocytes.
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11
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Chen H, Chakraborty AK, Kardar M. How nonuniform contact profiles of T cell receptors modulate thymic selection outcomes. Phys Rev E 2018; 97:032413. [PMID: 29776088 DOI: 10.1103/physreve.97.032413] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Indexed: 11/07/2022]
Abstract
T cell receptors (TCRs) bind foreign or self-peptides attached to major histocompatibility complex (MHC) molecules, and the strength of this interaction determines T cell activation. Optimizing the ability of T cells to recognize a diversity of foreign peptides yet be tolerant of self-peptides is crucial for the adaptive immune system to properly function. This is achieved by selection of T cells in the thymus, where immature T cells expressing unique, stochastically generated TCRs interact with a large number of self-peptide-MHC; if a TCR does not bind strongly enough to any self-peptide-MHC, or too strongly with at least one self-peptide-MHC, the T cell dies. Past theoretical work cast thymic selection as an extreme value problem and characterized the statistical enrichment or depletion of amino acids in the postselection TCR repertoire, showing how T cells are selected to be able to specifically recognize peptides derived from diverse pathogens yet have limited self-reactivity. Here, we investigate how the diversity of the postselection TCR repertoire is modified when TCRs make nonuniform contacts with peptide-MHC. Specifically, we were motivated by recent experiments showing that amino acids at certain positions of a TCR sequence have large effects on thymic selection outcomes, and crystal structure data that reveal a nonuniform contact profile between a TCR and its peptide-MHC ligand. Using a representative TCR contact profile as an illustration, we show via simulations that the statistical enrichment or depletion of amino acids now varies by position according to the contact profile, and, importantly, it depends on the implementation of nonuniform contacts during thymic selection. We explain these nontrivial results analytically. Our study has implications for understanding the selection forces that shape the functionality of the postselection TCR repertoire.
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Affiliation(s)
- Hanrong Chen
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Arup K Chakraborty
- Departments of Chemical Engineering, Chemistry, and Biological Engineering, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Mehran Kardar
- Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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12
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Organs on chip approach: a tool to evaluate cancer -immune cells interactions. Sci Rep 2017; 7:12737. [PMID: 28986543 PMCID: PMC5630614 DOI: 10.1038/s41598-017-13070-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 07/27/2017] [Indexed: 11/08/2022] Open
Abstract
In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a microfluidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from 'wild type' donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative confirmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems.
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13
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Effects of thymic selection on T cell recognition of foreign and tumor antigenic peptides. Proc Natl Acad Sci U S A 2017; 114:E7875-E7881. [PMID: 28874554 DOI: 10.1073/pnas.1708573114] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The advent of cancer immunotherapy has generated renewed hope for the treatment of many malignancies by introducing a number of novel strategies that exploit various properties of the immune system. These therapies are based on the idea that cytotoxic T lymphocytes (CTLs) directly recognize and respond to tumor-associated neoantigens (TANs) in much the same way as they would to foreign peptides presented on cell surfaces. To date, however, nearly all attempts to optimize immunotherapeutic strategies have been empirical. Here, we develop a model of T cell selection based on the assumption of random interaction strengths between a self-peptide and the various T cell receptors. The model enables the analytical study of the effects of selection on the CTL recognition of TANs and completely foreign peptides and can estimate the number of CTLs that can detect donor-matched transplants. We show that negative selection thresholds chosen to reflect experimentally observed thymic survival rates result in near-optimal production of T cells that are capable of surviving selection and recognizing foreign antigen. These analytical results are confirmed by simulation.
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14
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Abstract
This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.
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Affiliation(s)
- Arup K Chakraborty
- Institute for Medical Engineering and Science, Departments of Chemical Engineering, Physics, Chemistry, and Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; .,Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts 02139
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15
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Abstract
The self-nonself discrimination hypothesis remains a landmark concept in immunology. It proposes that tolerance breaks down in the presence of nonself antigens. In strike contrast, in statistics, occurrence of nonself elements in a sample (i.e., outliers) is not obligatory to violate the null hypothesis. Very often, what is crucial is the combination of (self) elements in a sample. The two views on how to detect a change seem challengingly different and it could seem difficult to conceive how immunological cellular interactions could trigger responses with a precision comparable to some statistical tests. Here it is shown that frustrated cellular interactions reconcile the two views within a plausible immunological setting. It is proposed that the adaptive immune system can be promptly activated either when nonself ligands are detected or self-ligands occur in abnormal combinations. In particular we show that cellular populations behaving in this way could perform location statistical tests, with performances comparable to t or KS tests, or even more general data mining tests such as support vector machines or random forests. In more general terms, this work claims that plausible immunological models should provide accurate detection mechanisms for host protection and, furthermore, that investigation on mechanisms leading to improved detection in “in silico” models can help unveil how the real immune system works.
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16
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Schlesinger KJ, Stromberg SP, Carlson JM. Coevolutionary immune system dynamics driving pathogen speciation. PLoS One 2014; 9:e102821. [PMID: 25054623 PMCID: PMC4108359 DOI: 10.1371/journal.pone.0102821] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 06/24/2014] [Indexed: 12/26/2022] Open
Abstract
We introduce and analyze a within-host dynamical model of the coevolution between rapidly mutating pathogens and the adaptive immune response. Pathogen mutation and a homeostatic constraint on lymphocytes both play a role in allowing the development of chronic infection, rather than quick pathogen clearance. The dynamics of these chronic infections display emergent structure, including branching patterns corresponding to asexual pathogen speciation, which is fundamentally driven by the coevolutionary interaction. Over time, continued branching creates an increasingly fragile immune system, and leads to the eventual catastrophic loss of immune control.
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Affiliation(s)
- Kimberly J. Schlesinger
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
- * E-mail:
| | - Sean P. Stromberg
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
| | - Jean M. Carlson
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
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17
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Agliari E, Barra A, Del Ferraro G, Guerra F, Tantari D. Anergy in self-directed B lymphocytes: A statistical mechanics perspective. J Theor Biol 2014; 375:21-31. [PMID: 24831414 DOI: 10.1016/j.jtbi.2014.05.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 04/11/2014] [Accepted: 05/02/2014] [Indexed: 11/16/2022]
Abstract
Self-directed lymphocytes may evade clonal deletion at ontogenesis but still remain harmless due to a mechanism called clonal anergy. For B-lymphocytes, two major explanations for anergy developed over the last decades: according to Varela theory, anergy stems from a proper orchestration of the whole B-repertoire, such that self-reactive clones, due to intensive feed-back from other clones, display strong inertia when mounting a response. Conversely, according to the model of cognate response, self-reacting cells are not stimulated by helper lymphocytes and the absence of such signaling yields anergy. Through statistical mechanics we show that helpers do not prompt activation of a sub-group of B-cells: remarkably, the latter are just those broadly interacting in the idiotypic network. Hence Varela theory can finally be reabsorbed into the prevailing framework of the cognate response model. Further, we show how the B-repertoire architecture may emerge, where highly connected clones are self-directed as a natural consequence of ontogenetic learning.
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Affiliation(s)
- Elena Agliari
- Dipartimento di Fisica, Sapienza Università di Roma, P.le A. Moro 2, 00185 Roma, Italy
| | - Adriano Barra
- Dipartimento di Fisica, Sapienza Università di Roma, P.le A. Moro 2, 00185 Roma, Italy.
| | - Gino Del Ferraro
- Department of Computational Biology, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
| | - Francesco Guerra
- Dipartimento di Fisica, Sapienza Università di Roma, P.le A. Moro 2, 00185 Roma, Italy
| | - Daniele Tantari
- Dipartimento di Matematica, Sapienza Università di Roma, P.le A. Moro 5, 00185 Roma, Italy
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18
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Abstract
The peripheral T cell repertoire is sculpted from prototypic T cells in the thymus bearing randomly generated T cell receptors (TCR) and by a series of developmental and selection steps that remove cells that are unresponsive or overly reactive to self-peptide–MHC complexes. The challenge of understanding how the kinetics of T cell development and the statistics of the selection processes combine to provide a diverse but self-tolerant T cell repertoire has invited quantitative modeling approaches, which are reviewed here.
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Affiliation(s)
- Andrew J Yates
- Departments of Systems and Computational Biology, Microbiology and Immunology, Albert Einstein College of Medicine , New York, NY , USA
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19
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Six A, Mariotti-Ferrandiz ME, Chaara W, Magadan S, Pham HP, Lefranc MP, Mora T, Thomas-Vaslin V, Walczak AM, Boudinot P. The past, present, and future of immune repertoire biology - the rise of next-generation repertoire analysis. Front Immunol 2013; 4:413. [PMID: 24348479 PMCID: PMC3841818 DOI: 10.3389/fimmu.2013.00413] [Citation(s) in RCA: 113] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Accepted: 11/12/2013] [Indexed: 01/09/2023] Open
Abstract
T and B cell repertoires are collections of lymphocytes, each characterized by its antigen-specific receptor. We review here classical technologies and analysis strategies developed to assess immunoglobulin (IG) and T cell receptor (TR) repertoire diversity, and describe recent advances in the field. First, we describe the broad range of available methodological tools developed in the past decades, each of which answering different questions and showing complementarity for progressive identification of the level of repertoire alterations: global overview of the diversity by flow cytometry, IG repertoire descriptions at the protein level for the identification of IG reactivities, IG/TR CDR3 spectratyping strategies, and related molecular quantification or dynamics of T/B cell differentiation. Additionally, we introduce the recent technological advances in molecular biology tools allowing deeper analysis of IG/TR diversity by next-generation sequencing (NGS), offering systematic and comprehensive sequencing of IG/TR transcripts in a short amount of time. NGS provides several angles of analysis such as clonotype frequency, CDR3 diversity, CDR3 sequence analysis, V allele identification with a quantitative dimension, therefore requiring high-throughput analysis tools development. In this line, we discuss the recent efforts made for nomenclature standardization and ontology development. We then present the variety of available statistical analysis and modeling approaches developed with regards to the various levels of diversity analysis, and reveal the increasing sophistication of those modeling approaches. To conclude, we provide some examples of recent mathematical modeling strategies and perspectives that illustrate the active rise of a "next-generation" of repertoire analysis.
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Affiliation(s)
- Adrien Six
- UPMC University Paris 06, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; CNRS, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; AP-HP, Hôpital Pitié-Salpêtrière, CIC-BTi Biotherapy , Paris , France ; AP-HP, Hôpital Pitié-Salpêtrière, Département Hospitalo-Universitaire (DHU), Inflammation-Immunopathology-Biotherapy (i2B) , Paris , France
| | - Maria Encarnita Mariotti-Ferrandiz
- UPMC University Paris 06, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; CNRS, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; AP-HP, Hôpital Pitié-Salpêtrière, Département Hospitalo-Universitaire (DHU), Inflammation-Immunopathology-Biotherapy (i2B) , Paris , France
| | - Wahiba Chaara
- UPMC University Paris 06, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; CNRS, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; AP-HP, Hôpital Pitié-Salpêtrière, CIC-BTi Biotherapy , Paris , France ; AP-HP, Hôpital Pitié-Salpêtrière, Département Hospitalo-Universitaire (DHU), Inflammation-Immunopathology-Biotherapy (i2B) , Paris , France
| | - Susana Magadan
- Institut National de la Recherche Agronomique, Unité de Virologie et Immunologie Moléculaires , Jouy-en-Josas , France
| | - Hang-Phuong Pham
- UPMC University Paris 06, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; CNRS, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France
| | - Marie-Paule Lefranc
- IMGT®, The International ImMunoGeneTics Information System®, Institut de Génétique Humaine, UPR CNRS 1142, Université Montpellier 2 , Montpellier , France
| | - Thierry Mora
- Laboratoire de Physique Statistique, UMR8550, CNRS and Ecole Normale Supérieure , Paris , France
| | - Véronique Thomas-Vaslin
- UPMC University Paris 06, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; CNRS, UMR 7211, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (I3) , Paris , France ; AP-HP, Hôpital Pitié-Salpêtrière, Département Hospitalo-Universitaire (DHU), Inflammation-Immunopathology-Biotherapy (i2B) , Paris , France
| | - Aleksandra M Walczak
- Laboratoire de Physique Théorique, UMR8549, CNRS and Ecole Normale Supérieure , Paris , France
| | - Pierre Boudinot
- Institut National de la Recherche Agronomique, Unité de Virologie et Immunologie Moléculaires , Jouy-en-Josas , France
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20
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Abstract
T cells orchestrate pathogen-specific adaptive immune responses by identifying peptides derived from pathogenic proteins that are displayed on the surface of infected cells. Host cells also display peptide fragments from the host's own proteins. Incorrectly identifying peptides derived from the body's own proteome as pathogenic can result in autoimmune disease. To minimize autoreactivity, immature T cells that respond to self-peptides are deleted in the thymus by a process called negative selection. However, negative selection is imperfect, and autoreactive T cells exist in healthy individuals. To understand how autoimmunity is yet avoided, without loss of responsiveness to pathogens, we have developed a model of T-cell training and response. Our model shows that T cells reliably respond to infection and avoid autoimmunity because collective decisions made by the T-cell population, rather than the responses of individual T cells, determine biological outcomes. The theory is qualitatively consistent with experimental data and yields a criterion for thymic selection to be adequate for suppressing autoimmunity.
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21
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Agliari E, Barra A, Bartolucci S, Galluzzi A, Guerra F, Moauro F. Parallel processing in immune networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 87:042701. [PMID: 23679445 DOI: 10.1103/physreve.87.042701] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Indexed: 06/02/2023]
Abstract
In this work, we adopt a statistical-mechanics approach to investigate basic, systemic features exhibited by adaptive immune systems. The lymphocyte network made by B cells and T cells is modeled by a bipartite spin glass, where, following biological prescriptions, links connecting B cells and T cells are sparse. Interestingly, the dilution performed on links is shown to make the system able to orchestrate parallel strategies to fight several pathogens at the same time; this multitasking capability constitutes a remarkable, key property of immune systems as multiple antigens are always present within the host. We also define the stochastic process ruling the temporal evolution of lymphocyte activity and show its relaxation toward an equilibrium measure allowing statistical-mechanics investigations. Analytical results are compared with Monte Carlo simulations and signal-to-noise outcomes showing overall excellent agreement. Finally, within our model, a rationale for the experimentally well-evidenced correlation between lymphocytosis and autoimmunity is achieved; this sheds further light on the systemic features exhibited by immune networks.
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Affiliation(s)
- Elena Agliari
- Dipartimento di Fisica, Università degli Studi di Parma, viale G. Usberti 7, 43100 Parma, Italy
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22
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Das J. Positive feedback produces broad distributions in maximum activation attained within a narrow time window in stochastic biochemical reactions. J Chem Phys 2013; 138:015101. [PMID: 23298061 DOI: 10.1063/1.4772583] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
How do single cell fate decisions induced by activation of key signaling proteins above threshold concentrations within a time interval are affected by stochastic fluctuations in biochemical reactions? We address this question using minimal models of stochastic chemical reactions commonly found in cell signaling and gene regulatory systems. Employing exact solutions and semi-analytical methods we calculate distributions of the maximum value (N) of activated species concentrations (P(max)(N)) and the time (t) taken to reach the maximum value (P(max)(t)) within a time interval in the minimal models. We find, the presence of positive feedback interactions make P(max)(N) more spread out with a higher "peakedness" in P(max)(t). Thus positive feedback interactions may help single cells to respond sensitively to a stimulus when cell decision processes require upregulation of activated forms of key proteins to a threshold number within a time window.
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Affiliation(s)
- Jayajit Das
- Battelle Center For Mathematical Medicine, The Research Institute at the Nationwide Childrens Hospital and Department of Pediatrics, and Biophysics Graduate Program, The Ohio State University, 700 Childrens Drive, Columbus, Ohio 43205, USA.
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23
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Agliari E, Asti L, Barra A, Ferrucci L. Organization and evolution of synthetic idiotypic networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 85:051909. [PMID: 23004790 DOI: 10.1103/physreve.85.051909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Indexed: 06/01/2023]
Abstract
We introduce a class of weighted graphs whose properties are meant to mimic the topological features of idiotypic networks, namely, the interaction networks involving the B core of the immune system. Each node is endowed with a bit string representing the idiotypic specificity of the corresponding B cell, and the proper distance between any couple of bit strings provides the coupling strength between the two nodes. We show that a biased distribution of the entries in bit strings can yield fringes in the (weighted) degree distribution, small-world features, and scaling laws, in agreement with experimental findings. We also investigate the role of aging, thought of as a progressive increase in the degree of bias in bit strings, and we show that it can possibly induce mild percolation phenomena, which are investigated too.
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Affiliation(s)
- Elena Agliari
- Dipartimento di Fisica, Università degli Studi di Parma, Parma, Italia
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24
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Wolfson MY, Nam K, Chakraborty AK. The effect of mutations on the alloreactive T cell receptor/peptide-MHC interface structure: a molecular dynamics study. J Phys Chem B 2011; 115:8317-27. [PMID: 21651302 PMCID: PMC3131071 DOI: 10.1021/jp202471d] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
T cells orchestrate adaptive, pathogen-specific immune responses. T cells have a surface receptor (called TCR) whose ligands are complexes (pMHCs) of peptides (derived from pathogens or host proteins) and major histocompatibility complex proteins (MHCs). MHC proteins vary between hosts. During organ transplants, host TCRs interact with peptides present in complex with genetically different MHCs. This usually causes a vigorous immune response: alloreactivity. Studies of alloreactive protein interactions have yielded results that present a puzzle. Some crystallographic studies concluded that the alloreactive TCR/MHC interface is essentially unaffected by changing the TCR peptide-binding region, suggesting that the peptide does not influence the interface. Another biochemical study concluded from mutation data that different peptides can alter the binding interface with the same TCR. To explore the origin of this puzzle, we used molecular dynamics simulations to study the dependence of the TCR/pMHC interface on changes in both the peptide and the TCR. Our simulations show that the footprint of the TCR on the pMHC is insensitive to mutations of the TCR peptide-binding loops, but peptide mutations can make multiple local changes to TCR/pMHC contacts. Therefore, our results demonstrate that the structural and mutation data do not conflict and reveal how subtle, but important, characteristics of the alloreactive TCR/pMHC interface are influenced by the TCR and the peptide.
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Affiliation(s)
| | - Kwangho Nam
- To whom correspondence should be addressed: ; , Phone: +1 617 495 8997; +1 617 253 3890. Fax: +1 617 495 8755; +1 617 253 2272
| | - Arup K. Chakraborty
- To whom correspondence should be addressed: ; , Phone: +1 617 495 8997; +1 617 253 3890. Fax: +1 617 495 8755; +1 617 253 2272
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25
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Roomp K, Domingues FS. Predicting interactions between T cell receptors and MHC-peptide complexes. Mol Immunol 2010; 48:553-62. [PMID: 21106246 DOI: 10.1016/j.molimm.2010.10.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2010] [Accepted: 10/24/2010] [Indexed: 12/30/2022]
Abstract
Conserved interactions between T cell receptors (TCRs) and major histocompatibility complex (MHC) proteins with bound peptide antigens are not well understood. In order to gain a better understanding of the interaction modes of human TCR variable (V) regions, we have performed a structural analysis of the TCRs bound to their MHC-peptide ligands in human, using the available structural models determined by X-ray crystallography. We identified important differences to previous studies in which such interactions were evaluated. Based on the interactions found in the actual experimental structures we developed the first rule-based approach for predicting the ability of TCR residues in the complementarity-determining region (CDR) 1, CDR2, and CDR3 loops to interact with the MHC-peptide antigen complex. Two relatively simple algorithms show good performance under cross validation.
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Affiliation(s)
- Kirsten Roomp
- Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbruecken, Germany.
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26
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Abstract
Higher organisms, such as humans, have an adaptive immune system that usually enables them to successfully combat diverse (and evolving) microbial pathogens. The adaptive immune system is not preprogrammed to respond to prescribed pathogens. Yet it mounts pathogen-specific responses against diverse microbes and establishes memory of past infections (the basis of vaccination). Although major advances have been made in understanding pertinent molecular and cellular phenomena, the mechanistic principles that govern many aspects of an immune response are not known. We illustrate how complementary approaches from the physical and life sciences can help confront this challenge. Specifically, we describe work that brings together statistical mechanics and cell biology to shed light on how key molecular/cellular components of the adaptive immune system are selected to enable pathogen-specific responses. We hope these examples encourage physical chemists to work at this crossroad of disciplines where fundamental discoveries with implications for human health might be made.
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Affiliation(s)
- Arup K Chakraborty
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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27
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Košmrlj A, Read EL, Qi Y, Allen TM, Altfeld M, Deeks SG, Pereyra F, Carrington M, Walker BD, Chakraborty AK. Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection. Nature 2010; 465:350-4. [PMID: 20445539 PMCID: PMC3098720 DOI: 10.1038/nature08997] [Citation(s) in RCA: 215] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2009] [Accepted: 03/11/2010] [Indexed: 01/21/2023]
Abstract
Without therapy, most people infected with human immunodeficiency virus (HIV) ultimately progress to AIDS. Rare individuals ('elite controllers') maintain very low levels of HIV RNA without therapy, thereby making disease progression and transmission unlikely. Certain HLA class I alleles are markedly enriched in elite controllers, with the highest association observed for HLA-B57 (ref. 1). Because HLA molecules present viral peptides that activate CD8(+) T cells, an immune-mediated mechanism is probably responsible for superior control of HIV. Here we describe how the peptide-binding characteristics of HLA-B57 molecules affect thymic development such that, compared to other HLA-restricted T cells, a larger fraction of the naive repertoire of B57-restricted clones recognizes a viral epitope, and these T cells are more cross-reactive to mutants of targeted epitopes. Our calculations predict that such a T-cell repertoire imposes strong immune pressure on immunodominant HIV epitopes and emergent mutants, thereby promoting efficient control of the virus. Supporting these predictions, in a large cohort of HLA-typed individuals, our experiments show that the relative ability of HLA-B alleles to control HIV correlates with their peptide-binding characteristics that affect thymic development. Our results provide a conceptual framework that unifies diverse empirical observations, and have implications for vaccination strategies.
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Affiliation(s)
- Andrej Košmrlj
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139
- Ragon Institute of MGH, MIT, & Harvard, Boston, MA 02129
| | - Elizabeth L. Read
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
- Ragon Institute of MGH, MIT, & Harvard, Boston, MA 02129
| | - Ying Qi
- Cancer and Inflammation Program, Laboratory of Experimental Immunology, SAIC-Frederick, Inc., NCI-Frederick, Fredrick, MD 21702
| | - Todd M. Allen
- Ragon Institute of MGH, MIT, & Harvard, Boston, MA 02129
| | - Marcus Altfeld
- Ragon Institute of MGH, MIT, & Harvard, Boston, MA 02129
| | | | | | - Mary Carrington
- Ragon Institute of MGH, MIT, & Harvard, Boston, MA 02129
- Cancer and Inflammation Program, Laboratory of Experimental Immunology, SAIC-Frederick, Inc., NCI-Frederick, Fredrick, MD 21702
| | - Bruce D. Walker
- Ragon Institute of MGH, MIT, & Harvard, Boston, MA 02129
- Howard Hughes Medical Institute, Chevy Chase, MD 20815
| | - Arup K. Chakraborty
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- Ragon Institute of MGH, MIT, & Harvard, Boston, MA 02129
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