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* Biological Engineering Division and
Department of Biology, Massachusetts Institute of Technology Cambridge, MA 02139; and
Department of Cellular and Molecular Medicine, School of Medical Sciences, University of Bristol, Bristol, United Kingdom
Proximal signaling events activated by TCR-peptide/MHC (TCR-pMHC) binding have been the focus of intense ongoing study, but understanding how the consequent downstream signaling networks integrate to govern ultimate avidity-appropriate TCR-pMHC T cell responses remains a crucial next challenge. We hypothesized that a quantitative combination of key downstream network signals across multiple pathways must encode the information generated by TCR activation, providing the basis for a quantitative model capable of interpreting and predicting T cell functional responses. To this end, we measured 11 protein nodes across six downstream pathways, along five time points from 10 min to 4 h, in a 1B6 T cell hybridoma stimulated by a set of three myelin proteolipid protein 139–151 altered peptide ligands. A multivariate regression model generated from this data compendium successfully comprehends the various IL-2 production responses and moreover successfully predicts a priori the response to an additional peptide treatment, demonstrating that TCR binding information is quantitatively encoded in the downstream network. Individual node and/or time point measurements less effectively accounted for the IL-2 responses, indicating that signals must be integrated dynamically across multiple pathways to adequately represent the encoded TCR signaling information. Of further importance, the model also successfully predicted a priori direct experimental tests of the effects of individual and combined inhibitors of the MEK/ERK and PI3K/Akt pathways on this T cell response. Together, our findings show how multipathway network signals downstream of TCR activation quantitatively integrate to translate pMHC stimuli into functional cell responses.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
1 This work was supported by a Computational and Systems Biology Initiative/Merck postdoctoral fellowship (to M.L.K.), grants from the National Institutes of Health (National Institute of General Medical Sciences Cell Decision Processes Center and National Institute of Allergy and Infectious Diseases R01), a gift from Entelos (to D.A.L.), and a grant from the National Multiple Sclerosis Society (to L.B.N.).
2 M.L.K. and L.W. contributed equally to this work.
3 Address correspondence and reprint requests to Dr. Douglas A. Lauffenburger, Room 56-341, 77 Massachusetts Avenue, Cambridge, MA 02139. E-mail address: lauffen{at}mit.edu
4 Abbreviations used in this paper: pMHC, peptide MHC; APL, altered peptide ligand; PLSR, partial least squares regression; IKK, I
B kinase.
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