Modeling T Cell Antigen Discrimination Based on Feedback Control of Digital ERK Responses
By G. Altan-Bonnet, Ronald N. Germain. PLoS Biology, 2005
508 citations recorded by the source corpus.
T-lymphocyte activation displays a remarkable combination of speed, sensitivity, and discrimination in response to peptide–major histocompatibility complex (pMHC) ligand engagement of clonally distributed antigen receptors (T cell receptors or TCRs). Even a few foreign pMHCs on the surface of an antigen-presenting cell trigger effective signaling within seconds, whereas 1 × 105–1 × 106 self-pMHC ligands that may differ from the foreign stimulus by only a single amino acid fail to elicit this response. No existing model accounts for this nearly absolute distinction between closely related TCR ligands while also preserving the other canonical features of T-cell responses. Here we document the unexpected highly amplified and digital nature of extracellular signal-regulated kinase (ERK) activation in T cells. Based on this observation and evidence that competing positive- and negative-feedback loops contribute to TCR ligand discrimination, we constructed a new mathematical model of proximal TCR-dependent signaling. The model made clear that competition between a digital positive feedback based on ERK activity and an analog negative feedback involving SH2 domain-containing tyrosine phosphatase (SHP-1) was critical for defining a sharp ligand-discrimination threshold while preserving a rapid and sensitive response. Several nontrivial predictions of this model, including the notion that this threshold is highly sensitive to small changes in SHP-1 expression levels during cellular differentiation, were confirmed by experiment. These results combining computation and experiment reveal that ligand discrimination by T cells is controlled by the dynamics of competing feedback loops that regulate a high-gain digital amplifier, which is itself modulated during differentiation by alterations in the intracellular concentrations of key enzymes. The organization of the signaling network that we model here may be a prototypic solution to the problem of achieving ligand selectivity, low noise, and high sensitivity in biological responses.
Subfields: Immunology
View Paper: https://www.semanticscholar.org/paper/3fada9aa03204a031f2c4207ec6007ad04d4581f
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In Immunology
Citations in the corpus
- A Correction to the Review Titled "Rules for Modeling Signal-Transduction Systems" by W. S. Hlavacek et al. (2006, background): “Reaction-scheme diagrams that have been drawn for signal-transduction systems, some of which are quite large ( 47 , 48 ) ( 39 , 40 ).”
- A Logical Model Provides Insights into T Cell Receptor Signaling (2007, background): “Note that there are kinetic models dealing with a smaller part of the network (e.g., [5,21,22]), as well as models of the gene regulatory network governing T cell activation [23].”
- Quantitative challenges in understanding ligand discrimination by αβ T cells (2008, methodology): “Using naïve lymphocytes that have been activated ex vivo for one day and rested after APC removal for five days, or activated ex vivo and expanded for 5 days [4], responsiveness towards sub-threshold ligands was tested.”