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Robins E, Zheng M, Ni Q, Liu S, Liang C, Zhang B, Guo J, Zhuang Y, He YW, Zhu P, Wan Y, Li QJ. Conversion of effector CD4 + T cells to a CD8 + MHC II-recognizing lineage. Cell Mol Immunol 2020; 18:150-161. [PMID: 32066854 DOI: 10.1038/s41423-019-0347-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 11/27/2019] [Indexed: 12/22/2022] Open
Abstract
CD4+ and CD8+ T cells are dichotomous lineages in adaptive immunity. While conventionally viewed as distinct fates that are fixed after thymic development, accumulating evidence indicates that these two populations can exhibit significant lineage plasticity, particularly upon TCR-mediated activation. We define a novel CD4-CD8αβ+ MHC II-recognizing population generated by lineage conversion from effector CD4+ T cells. CD4-CD8αβ+ effector T cells downregulated the expression of T helper cell-associated costimulatory molecules and increased the expression of cytotoxic T lymphocyte-associated cytotoxic molecules. This shift in functional potential corresponded with a CD8+-lineage skewed transcriptional profile. TCRβ repertoire sequencing and in vivo genetic lineage tracing in acutely infected wild-type mice demonstrated that CD4-CD8αβ+ effector T cells arise from fundamental lineage reprogramming of bona fide effector CD4+ T cells. Impairing autophagy via functional deletion of the initiating kinase Vps34 or the downstream enzyme Atg7 enhanced the generation of this cell population. These findings suggest that effector CD4+ T cells can exhibit a previously unreported degree of skewing towards the CD8+ T cell lineage, which may point towards a novel direction for HIV vaccine design.
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Affiliation(s)
- Elizabeth Robins
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA.,Pelotonia Institute for Immuno-Oncology, Comprehensive Cancer Center, Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Ming Zheng
- Department of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, China
| | - Qingshan Ni
- Biomedical Analysis Center, Third Military Medical University, Chongqing, China
| | - Siqi Liu
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Chen Liang
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Baojun Zhang
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Jian Guo
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Yuan Zhuang
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA
| | - You-Wen He
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Ping Zhu
- Department of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, China
| | - Ying Wan
- Biomedical Analysis Center, Third Military Medical University, Chongqing, China
| | - Qi-Jing Li
- Department of Immunology, Duke University Medical Center, Durham, NC, 27710, USA.
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4
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Milam AAV, Bartleson JM, Donermeyer DL, Horvath S, Durai V, Raju S, Yu H, Redmann V, Zinselmeyer B, White JM, Murphy KM, Allen PM. Tuning T Cell Signaling Sensitivity Alters the Behavior of CD4 + T Cells during an Immune Response. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2018; 200:3429-3437. [PMID: 29618523 PMCID: PMC5940509 DOI: 10.4049/jimmunol.1701422] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 03/13/2018] [Indexed: 11/19/2022]
Abstract
Intricate processes in the thymus and periphery help curb the development and activation of autoreactive T cells. The subtle signals that govern these processes are an area of great interest, but tuning TCR sensitivity for the purpose of affecting T cell behavior remains technically challenging. Previously, our laboratory described the derivation of two TCR-transgenic CD4 T cell mouse lines, LLO56 and LLO118, which recognize the same cognate Listeria epitope with the same affinity. Despite the similarity of the two TCRs, LLO56 cells respond poorly in a primary infection whereas LLO118 cells respond robustly. Phenotypic examination of both lines revealed a substantial difference in their surface of expression of CD5, which serves as a dependable readout of the self-reactivity of a cell. We hypothesized that the increased interaction with self by the CD5-high LLO56 was mediated through TCR signaling, and was involved in the characteristic weak primary response of LLO56 to infection. To explore this issue, we generated an inducible knock-in mouse expressing the self-sensitizing voltage-gated sodium channel Scn5a. Overexpression of Scn5a in peripheral T cells via the CD4-Cre promoter resulted in increased TCR-proximal signaling. Further, Scn5a-expressing LLO118 cells, after transfer into BL6 recipient mice, displayed an impaired response during infection relative to wild-type LLO118 cells. In this way, we were able to demonstrate that tuning of TCR sensitivity to self can be used to alter in vivo immune responses. Overall, these studies highlight the critical relationship between TCR-self-pMHC interaction and an immune response to infection.
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Affiliation(s)
- Ashley A Viehmann Milam
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - Juliet M Bartleson
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - David L Donermeyer
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - Stephen Horvath
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - Vivek Durai
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - Saravanan Raju
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - Haiyang Yu
- Ludwig Institute for Cancer Research, La Jolla, CA 92093; and
| | - Veronika Redmann
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - Bernd Zinselmeyer
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - J Michael White
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
| | - Kenneth M Murphy
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110
- Howard Hughes Medical Institute, Washington University School of Medicine, St. Louis, MO 63110
| | - Paul M Allen
- Division of Immunobiology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110;
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5
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Milam AV, Allen PM. Functional Heterogeneity in CD4(+) T Cell Responses Against a Bacterial Pathogen. Front Immunol 2015; 6:621. [PMID: 26697015 PMCID: PMC4675919 DOI: 10.3389/fimmu.2015.00621] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 11/30/2015] [Indexed: 11/13/2022] Open
Abstract
To investigate how CD4+ T cells function against a bacterial pathogen, we generated a Listeria monocytogenes-specific CD4+ T cell model. In this system, two TCRtg mouse lines, LLO56 and LLO118, recognize the same immunodominant epitope (LLO190-205) of L. monocytogenes and have identical in vitro responses. However, in vivo LLO56 and LLO118 display vastly different responses during both primary and secondary infection. LLO118 dominates in the primary response and in providing CD8 T cell help. LLO56 predominates in the secondary response. We have also shown that both specific [T cell receptor (TCR)-mediated] and non-specific stimuli (bypassing the TCR) elicit distinct responses from the two transgenics, leading us to conclude that the strength of self-pMHC signaling during development tightly dictates the cell’s future response in the periphery. Herein, we review our findings in this transfer system, focusing on the contribution of the immunomodulatory molecule CD5 and the importance of self-interaction in peripheral maintenance of the cell. We also discuss the manner in which individual TCR affinities to foreign and self-pMHC contribute to the outcome of an immune response; our assertion is that there exists a spectrum of possible T cell responses to recognition of cognate antigen during infection, adding immense diversity to the immune system’s response to pathogens.
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Affiliation(s)
- Ashley Viehmann Milam
- Department of Pathology and Immunology, Washington University School of Medicine , St. Louis, MO , USA
| | - Paul M Allen
- Department of Pathology and Immunology, Washington University School of Medicine , St. Louis, MO , USA
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6
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Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, Philipson CW, Hoops S, Marathe M, Eubank S, Bisset K, Wendelsdorf K, Jarrah A, Mei Y, Bassaganya-Riera J. Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity. PLoS Comput Biol 2013; 9:e1003027. [PMID: 23592971 PMCID: PMC3617204 DOI: 10.1371/journal.pcbi.1003027] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2012] [Accepted: 02/23/2013] [Indexed: 11/18/2022] Open
Abstract
Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa. CD4+ T cells can differentiate into different phenotypes depending on the cytokine milieu. Due to the complexity of this process, we have constructed a computational and mathematical model with sixty ordinary differential equations representing a CD4+ T cell differentiating into either Th1, Th2, Th17 or iTreg cells. The model includes cytokines, nuclear receptors and transcription factors that define fate and function of CD4+ T cells. Computational simulations illustrate how a proinflammatory Th17 cell can undergo reprogramming into an anti-inflammatory iTreg phenotype following PPARγ activation. This modeling-derived hypothesis has been validated with in vitro and in vivo experiments. Experimental data support the modeling-derived prediction and demonstrate that the loss of PPARγ enhances a proinflammatory response characterized by Th17 in colitis-induced mice. Moreover, pharmacological activation of PPARγ in vivo can affect the Th17/iTreg balance by upregulating FOXP3 and downregulating IL-17A and RORγt. In summary, we demonstrate that computational simulations using our CD4+ T cell model provide novel unforeseen hypotheses related to the molecular mechanisms controlling differentiation and function of CD4+ T cells. In vivo findings validated the modeling prediction that PPARγ modulates differentiation and plasticity of CD4+ T cells in mice.
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Affiliation(s)
- Adria Carbo
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Raquel Hontecillas
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Barbara Kronsteiner
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Monica Viladomiu
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Mireia Pedragosa
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Pinyi Lu
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Casandra W. Philipson
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Stefan Hoops
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Madhav Marathe
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Stephen Eubank
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Keith Bisset
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Katherine Wendelsdorf
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Abdul Jarrah
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Yongguo Mei
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Josep Bassaganya-Riera
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, Virginia, United States of America
- * E-mail:
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