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Quantitative analysis of the T cell receptor signaling network in response to altered peptide ligands

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dc.contributor.advisor Douglas A. Lauffenburger. en_US
dc.contributor.author Wille, Lucia en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Biology. en_US
dc.date.accessioned 2006-11-07T13:06:44Z
dc.date.available 2006-11-07T13:06:44Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/34576
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2006. en_US
dc.description Vita. en_US
dc.description Includes bibliographical references (p. 89-100). en_US
dc.description.abstract Understanding the adaptive immune system poses a great conceptual challenge. It has evolved to respond to foreign invaders with exquisite sensitivity and selectivity. In particular, the T cell branch of the immune system is trained to distinguish between self and non-self. This requires that a single receptor, the T cell receptor, bind to multiple ligands resulting in different cell fates, based in part on the avidity of the ligand. To address the question of ligand affinity discrimination in T cells, several T cell lines, both mouse and human, were screened for their ability to exhibit multiple cell fates in response to stimulation through the T cell receptor. A hybridoma system was identified that exhibits different levels of both apoptosis and cytokine production in response to three altered peptide ligands. We investigated how the consequent downstream signaling networks integrate to ultimately govern avidity-appropriate T cell responses in this hybridoma system. Here, we hypothesized that a quantitative combination of key downstream network signals can effectively represent the information processing generated by TCR ligation, providing a model capable of interpreting and predicting T cell functional responses. en_US
dc.description.abstract (cont.) We generated a multivariate regression model from over 1100 signaling measurements that could predict IL-2 production in response, to a new ligand condition, implying that peptide avidity information is encoded in the magnitudes of downstream signals. Our model predicted a priori that IL-2 production is quantitatively related to both known and novel modulators, Erk and Akt, with verification by subsequent inhibitor studies. Furthermore, the model could predict the non-additive effect of inhibiting both molecules simultaneously. These findings demonstrate the power of our quantitative systems modeling approach for numerical prediction of T cell responses from a key set of dynamic signals, and conceptual understanding of how complex signaling networks integrate information to translate pMHC stimuli into functional responses. To further analyze the relationship between Erk levels and IL-2 production in T cells, an epiallelic series of stable Erkl and Erk2 knockdowns was generated. We show that both total and phospho Erk levels are correlated with IL-2 production and provide a framework for interpreting the phenotypes of partial knockdowns in other systems. en_US
dc.description.statementofresponsibility by Lucia Wille. en_US
dc.format.extent 107 p. en_US
dc.format.extent 13560659 bytes
dc.format.extent 13560167 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Biology. en_US
dc.title Quantitative analysis of the T cell receptor signaling network in response to altered peptide ligands en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Biology. en_US
dc.identifier.oclc 71205262 en_US


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