Modeling T Cell Antigen Discrimination Based on Feedback Control of Digital ERK Responses

Lymphocyte Biology Section, Laboratory of Immunology, National Institute of Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, United States of America.
PLoS Biology (Impact Factor: 9.34). 12/2005; 3(11):e356. DOI: 10.1371/journal.pbio.0030356
Source: PubMed


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 x 10(5)-1 x 10(6) 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.

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Available from: Grégoire Altan-Bonnet, Jan 06, 2014
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    • "Table S4 in File S2 includes citations of References [73], [74], [75], [76], [77], [78], [79]. Table S5 in File S2 includes citations of References [80], [81], [82], [83], [84], [85]. The Supplementary Text S1 (File S3) includes citations of References [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140], [141], [142], [143], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157], [158], [159], [160], [161], [162], [163], [164], [165], [166], [167], [168], [169], [170], [171], [172], [173], [174], [175], [176], [177], [178], [179], [180], [181], [182], [183], [184], [185], [186], [187], [188], [189], [190], [191], [192], [193], [194], [195], [196], [197], [198], [199], [200], [201], [202], [203], [204], [205], [206], [207], [208], [209], [210], [211], [212], [213], [214], [215], [216], [217], [218], [219], [220], [221], [222]. "
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    ABSTRACT: In adaptive immune responses, T-cell receptor (TCR) signaling impacts multiple cellular processes and results in T-cell differentiation, proliferation, and cytokine production. Although individual protein-protein interactions and phosphorylation events have been studied extensively, we lack a systems-level understanding of how these components cooperate to control signaling dynamics, especially during the crucial first seconds of stimulation. Here, we used quantitative proteomics to characterize reshaping of the T-cell phosphoproteome in response to TCR/CD28 co-stimulation, and found that diverse dynamic patterns emerge within seconds. We detected phosphorylation dynamics as early as 5 s and observed widespread regulation of key TCR signaling proteins by 30 s. Development of a computational model pointed to the presence of novel regulatory mechanisms controlling phosphorylation of sites with central roles in TCR signaling. The model was used to generate predictions suggesting unexpected roles for the phosphatase PTPN6 (SHP-1) and shortcut recruitment of the actin regulator WAS. Predictions were validated experimentally. This integration of proteomics and modeling illustrates a novel, generalizable framework for solidifying quantitative understanding of a signaling network and for elucidating missing links.
    PLoS ONE 08/2014; 9(8):e104240. DOI:10.1371/journal.pone.0104240 · 3.23 Impact Factor
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    • "This accounts well for the specificity, sensitivity and speed of T-cell discrimination but the authors went further. They interrogated the fitted model to make predictions about issues such as antagonism and tunability and they confirmed these with new experiments [29]. The model was repeatedly forced to put its falsifiability on the line. "
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    ABSTRACT: : In this essay I will sketch some ideas for how to think about models in biology. I will begin by trying to dispel the myth that quantitative modeling is somehow foreign to biology. I will then point out the distinction between forward and reverse modeling and focus thereafter on the former. Instead of going into mathematical technicalities about different varieties of models, I will focus on their logical structure, in terms of assumptions and conclusions. A model is a logical machine for deducing the latter from the former. If the model is correct, then, if you believe its assumptions, you must, as a matter of logic, also believe its conclusions. This leads to consideration of the assumptions underlying models. If these are based on fundamental physical laws, then it may be reasonable to treat the model as 'predictive', in the sense that it is not subject to falsification and we can rely on its conclusions. However, at the molecular level, models are more often derived from phenomenology and guesswork. In this case, the model is a test of its assumptions and must be falsifiable. I will discuss three models from this perspective, each of which yields biological insights, and this will lead to some guidelines for prospective model builders.
    BMC Biology 04/2014; 12(1):29. DOI:10.1186/1741-7007-12-29 · 7.98 Impact Factor
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    • "Protein phosphorylation, the hallmark in the control of protein function, is a reversible and dynamic process. In many cellular signal transduction pathways, it is known that kinases are often responsible for the onset and/or activation of signaling while phosphatases are mainly involved in the adaptation and noise control of signaling [1] [2]. Protein phosphatases remove covalently attached phosphate groups from Ser, Thr, and Tyr residues in target proteins. "
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    ABSTRACT: The specificity and efficiency of cell signaling is largely governed by the complex formation of signaling proteins. The precise spatio-temporal control of the complex assembly is crucial for proper signaling and cell survival. Protein phosphorylation is a key mechanism of signal processing in most of cell signaling networks. Phosphatases, along with kinases, control the phosphorylation state of many proteins and thus play a critical role in the precise regulation of signaling at each stage such as activation, propagation, and adaptation. Identification and functional analysis of pathway-specific phosphatase is, therefore, crucial for the understanding of cell signaling mechanisms. Here, we have developed a novel screening strategy to identify pathway-specific phosphatases, in which the entire repertoire of cell's phosphatases was tethered to a signaling complex and the changes in the signaling response were monitored. As a model target, we have chosen the mating MAP kinase pathway in the budding yeast, which is composed of three kinases and Ste5 scaffold protein. Using this strategy, a putative Ser/Thr phosphatase, Ppq1, was identified to be mating-specific. Results show that Ppq1 down-regulates the mating response by targeting at or upstream of the terminal MAP kinase Fus3 in the cascade. The catalytic activity of Ppq1 as a phosphatase was confirmed in vitro and is necessary for its function in the regulation of mating signaling. Overall, the data suggest that Ppq1 functions as a negative regulator of mating MAPK pathway by dephosphorylating of target pathway protein and plays a key role in the control of the background signaling noise.
    Biochemical and Biophysical Research Communications 12/2013; 443(1). DOI:10.1016/j.bbrc.2013.11.110 · 2.30 Impact Factor
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