Quantitative Network Signal Combinations Downstream of TCR Activation Can Predict IL-2 Production Response 1 Melissa L. Kemp, 2 * Lucia Wille, 2† Christina L. Lewis,* Lindsay B. Nicholson, ‡ and Douglas A. Lauffenburger 3 * † 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 compen- dium 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 mul- tipathway network signals downstream of TCR activation quantitatively integrate to translate pMHC stimuli into functional cell responses. The Journal of Immunology, 2007, 178: 4984 – 4992. S ignaling through the TCR leading to cell decision pro- cesses is determined in part by the affinity of the TCR for its ligand. Affinities of TCR/ligand interactions can differ by at least three orders of magnitude (1) and can correspondingly elicit vastly different cellular responses. In mature T cells, high- affinity TCR-peptide MHC (pMHC) 4 interactions can lead to T cell activation, whereas low-affinity interactions are important for cel- lular homeostasis and may be involved in antigenic signal ampli- fication (2– 4). Activation of T cells by altered peptide ligands (APL) can also induce partial activation, hyperstimulation, and anergy (5). Elucidating the complex molecular details underlying ligand affinity discrimination by T cells is central to our under- standing of both T cell-mediated immunity to pathogens and im- mune dysfunctions, such as autoimmunity and aberrant immunosurveillance. Cell fate responses to TCR ligation can vary with single residue changes occurring on the peptide at the ligand-receptor interface. Upon TCR engagement, changes in the ITAM phosphorylation patterns (6, 7) activate multiple interconnected signaling pathways (8, 9) (illustrated in Fig. 1A). Propagation of stimuli from the re- ceptor results in T cell activation and nuclear localization of AP-1, NFAT, NF-B, and Oct-1 transcription factors. One early marker of T cell activation is IL-2 production. AP-1, NFAT, NF-B, and Oct-1 transcription factors are all known to bind to the IL-2 pro- moter region (10). A growing body of work has focused on proximal TCR signal- ing events to individual downstream pathways to elucidate mech- anisms of ligand affinity discrimination (3). However, there exists little understanding about how the dynamic state of the multipath- way network activated by TCR signaling, as well as by costimu- latory cues, may explicitly encode the information crucial for the ultimate cell behavioral responses governed discriminatorily by TCR ligation events. It is worth noting that recent studies in other areas of cell biology have ascertained that quantitative combina- tions of nodes in multiple downstream pathways across a signaling network distal to initiating receptor activation integratively repre- sent the receptor-mediated information governing cell phenotypic behavior (11–13). In this study, we propose that in the context of TCR stimulation, activation states quantitatively encoded by the signaling network define regulatory aspects for a T cell cytokine production response. To test this hypothesis, a T cell hybridoma line was stimulated with APL and a quantitative data set of both signaling dynamics and cellular response data was generated across the full set of APL treatments. IL-2 production was chosen as a relevant cell func- tional response, and signaling network nodes were defined on the *Biological Engineering Division and † Department of Biology, Massachusetts Insti- tute of Technology Cambridge, MA 02139; and ‡ Department of Cellular and Molec- ular Medicine, School of Medical Sciences, University of Bristol, Bristol, United Kingdom Received for publication August 11, 2006. Accepted for publication January 29, 2007. 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@mit.edu 4 Abbreviations used in this paper: pMHC, peptide MHC; APL, altered peptide li- gand; PLSR, partial least squares regression; IKK, IB kinase. 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