Quantitative Modeling of Lymphocyte Signaling Pathways

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In order to further understanding of T cell responses to antigen stimulation, we seek to generate quantitative models and simulations of the activation of the major signaling pathways engaged upon T cell receptor (TCR) activation. Our current experimental model is CD4+ T cells isolated from 5C.C7 mice, and we have established reproducible protocols for activating these cells using the APC cell line P13.9. We have generated protein standards for quantification of Raf1, Mek1+2, Erk1+2 and Ksr by expressing the murine cDNAs as tagged fusions in bacterial and/or mammalian cells. The optimal expression and purification method has been identified in each case to give acceptable quantities of pure protein for loading reference samples on quantitative western blots. Multiple commercial antibody sources for these targets have been assessed to identify suitable antisera for both western and flow cytometry measurements. We have also assessed the suitability of the xMAP (multi-analyte profiling) technology developed by Luminex for quantitative assessment of protein concentration and phosphorylation. So far, this has provided high quality data for several components of the cascade. We intend to use all of the above approaches to increase confidence in our estimations of both protein number and degree of protein phosphorylation in response to stimulus. Thus far, the data we have obtained for the Mek and Erk proteins expressed in 5C.C7 CD4+ T cells by both xMAP and quantitative western blotting have shown good agreement. Our estimations of protein content by these methods will be complemented by quantitative proteomic analyses carried out by the LSB Cellular Networks Proteomics unit. Another key requirement in this project is to acquire an estimation of the morphology and geometric characteristics of the T cell to allow accurate calculations of the concentrations of the signaling proteins in the cell. This has been achieved through staining the 5C.C7 CD4+ T cells with selective dyes and expression of a panel of subcellular markers that localize to specific regions of the cell. In collaboration with the NIAID RTB imaging core laboratory, we have used confocal microscopy and 3D cell reconstruction software to generate reproducible data on cell shape characteristics and volume. We have identified suitable antisera for flow cytometry detection of the Erk cascade proteins ERK2, MEK2, Raf1, Grb2 and phospho-ERK2. This is important as it provides single cell data and an indication of the population distribution of protein concentrations and phosphorylation responses. Erk2 represents the major Erk isoform expressed in CD4+ T cells isolated from 5C.C7 mice, and we are using the phosphorylation status of this protein as a key readout in our modeling of TCR activation. We have determined the phosphorylation dynamics of Erk2 in response to APC activation by flow cytometry, and have complemented this data with FRET measurements using the EKAR reporter to provide spatial data on Erk2 phosphorylation. This year we have extended these studies to a hybridoma cell line (2B4) that expresses the same TCR as the primary cells isolated from 5C.C7 transgenic mice. The 2B4 cell line was re-cloned from single cells to identify a fraction that was CD4+ and gave consistent Erk2 phosphorylation and Ca2+ responses when activated with the same APC cell line P13.9. This provides a consistent supply of cells that show comparable response characteristics to the primary cells. It also provides a better model system for stable expression of signaling reporters, and accordingly, we have generated 2B4 lines that express the EKAR Erk reporter localized to either the cytoplasm or nucleus. This has the potential to provide important spatial data on Erk activity after TCR challenge with strong and weak peptide agonists. We have also found the 2B4 hybridoma to be a useful model for analysis of single cell Ca2+ release in response to varying strengths of TCR activation. We are currently analyzing this data to determine how it might inform existing models of TCR ligand discrimination.