Antagonism of superantigen-stimulated helper T-cell clones and hybridomas by altered peptide ligand.

Evavold, Brian D.; Sloan‐Lancaster, Joanne; Allen, Paul M.

doi:10.1073/pnas.91.6.2300

Cited by 64 publications

(34 citation statements)

References 36 publications

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“…Another interesting response of T cells to pMHC molecules is called antagonism (15,20). Antagonists are pMHC molecules obtained by mutating agonist peptide residues.…”

Section: Signaling Modelmentioning

confidence: 99%

Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities

Artyomov

Das²,

Kardar

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

109

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Detection of different extracellular stimuli leading to functionally distinct outcomes is ubiquitous in cell biology, and is often mediated by differential regulation of positive and negative feedback loops that are a part of the signaling network. In some instances, these cellular responses are stimulated by small numbers of molecules, and so stochastic effects could be important. Therefore, we studied the influence of stochastic fluctuations on a simple signaling model with dueling positive and negative feedback loops. The class of models we have studied is characterized by single deterministic steady states for all parameter values, but the stochastic response is bimodal; a behavior that is distinctly different from models studied in the context of gene regulation. For example, when positive and negative regulation is roughly balanced, a unique deterministic steady state with an intermediate value for the amount of a downstream signaling product is found. However, for small numbers of signaling molecules, stochastic effects result in a bimodal distribution for this quantity, with neither mode corresponding to the deterministic solution; i.e., cells are in ''on'' or ''off'' states, not in some intermediate state. For a large number of molecules, the stochastic solution converges to the mean-field result. When fluctuations are important, we find that signal output scales with control parameters ''anomalously'' compared with mean-field predictions. The necessary and sufficient conditions for the phenomenon we report are quite common. So, our findings are expected to be of broad relevance, and suggest that stochastic effects can enable binary cellular decisions.bimodality ͉ fluctuations

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“…Another interesting response of T cells to pMHC molecules is called antagonism (15,20). Antagonists are pMHC molecules obtained by mutating agonist peptide residues.…”

Section: Signaling Modelmentioning

confidence: 99%

Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities

Artyomov

Das²,

Kardar

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

109

View full text Add to dashboard Cite

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“…The 2.102 T cells respond vigorously to murine hemoglobin, are specific for Hb (64 -76)/I-E k , and have been used in previous studies examining altered peptide ligands, alloreactivity, and calcium signaling (34,(42)(43)(44)(45)(46). Intracellular cytokine staining confirmed proper polarization to the desired Th cell subtype by measuring their signature cytokines IFN-␥, IL-4, and IL-17 and representative histograms for each T cell subtype are shown (Fig.…”

Section: Single-cell Calcium Measurementsmentioning

confidence: 99%

Th17 Cells Exhibit a Distinct Calcium Profile from Th1 and Th2 Cells and Have Th1-Like Motility and NF-AT Nuclear Localization

Weber

Miller

Allen

2008

The Journal of Immunology

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Helper T cell subsets have evolved to respond to different pathogens, and upon activation secrete distinct sets of cytokines. The discovery and identification of Th17 cells, which develop via a unique lineage from Th1 and Th2 cells, have provided new insights into aspects of immune regulation and host defense that were previously unclear. A key early signaling event upon Ag recognition is elevation of intracellular free Ca2+, and cytokine expression can be differentially induced depending on the duration, amplitude, and pattern of Ca2+ signaling. Th1 and Th2 cells can be distinguished by their Ca2+ profiles, and we provide in this study the first report regarding Ca2+ signaling in Th17 cells. Th17 cells have a distinct Ca2+ signaling profile from Th1 and Th2 cells with intermediate sustained Ca2+ levels and increased oscillations compared with Th2 cells. Elevated intracellular Ca2+ has been shown to inhibit T cell motility, and we observed that Th17 cells, like Th1 cells, are less motile than Th2 cells. Analysis of NF-AT nuclear localization revealed that Th1 and Th17 cells have significantly higher levels at later time points compared with Th2 cells. Thus, these findings show that Th17 cells, in addition to their distinct cytokine response from Th1 and Th2 cells, display unique patterns of intracellular Ca2+ signaling and Th1-like motility behavior and nuclear localization of NF-AT.

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“…For example, Burroughs et al (2006) have recently studied a model in which spatial segregation of kinase and phosphatase activities contributes to TCR signaling. It is now clear that the original kinetic proofreading model is unable to explain all aspects of ligand discrimination by the TCR, including the phenomenon of antagonism (Evavold et al, 1994). Antagonism occurs when a non-stimulatory ligand inhibits T cell activation signals that are generated by an agonist ligand.…”

mentioning

confidence: 99%

Stochastic effects and bistability in T cell receptor signaling

Lipniacki

Hat

Faeder

et al. 2008

Journal of Theoretical Biology

119

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The stochastic dynamics of T cell receptor (TCR) signaling are studied using a mathematical model intended to capture kinetic proofreading (sensitivity to ligand-receptor binding kinetics) and negative and positive feedback regulation mediated respectively by the phosphatase SHP1 and the MAP kinase ERK. The model incorporates protein-protein interactions involved in initiating TCR-mediated cellular responses and reproduces several experimental observations about the behavior of TCR signaling, including robust responses to as few as a handful of ligands (agonist peptide-MHC complexes on an antigen-presenting cell), distinct responses to ligands that bind TCR with different lifetimes, and antagonism. Analysis of the model indicates that TCR signaling dynamics are marked by significant stochastic fluctuations and bistability, which is caused by the competition between the positive and negative feedbacks. Stochastic fluctuations are such that single-cell trajectories differ qualitatively from the trajectory predicted in the deterministic approximation of the dynamics. Because of bistability, the average of single-cell trajectories differs markedly from the deterministic trajectory. Bistability combined with stochastic fluctuations allows for switch-like responses to signals, which may aid T cells in making committed cell-fate decisions.

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Antagonism of superantigen-stimulated helper T-cell clones and hybridomas by altered peptide ligand.

Abstract: T-cell activation by an immunogenic peptide can be antagonized by nonstimulatory analogs of that peptide.We investigated this T-cell receptor antagonism by using staphylococcal enterotoxin superantigen to stimulate hemoglobin-specific helper T (Tib) cells

Cited by 64 publications

References 36 publications

Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities

Purely stochastic binary decisions in cell signaling models without underlying deterministic bistabilities

Th17 Cells Exhibit a Distinct Calcium Profile from Th1 and Th2 Cells and Have Th1-Like Motility and NF-AT Nuclear Localization

Stochastic effects and bistability in T cell receptor signaling

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