The tyrosine phosphorylated epidermal growth factor receptor (EGFR) initiates numerous cell signaling pathways. and cell interior and point to new possibilities for targeting PTPs for modulation of EGFR dynamics. Introduction The binding of SH2- and PTB-domain-containing proteins to phosphorylated C-terminal tyrosines of the epidermal growth factor receptor (EGFR) links the receptor to cell-signaling pathways and to receptor trafficking mechanisms (1). Whereas the processes leading to EGFR tyrosine phosphorylation have been studied in detail, relatively little is known about quantitative aspects of receptor dephosphorylation by protein tyrosine phosphatases (PTPs) (2,3). Estimates of the rates of EGFR tyrosine dephosphorylation are limited (4C6), and the extent to which individual PTPs contribute to the net dephosphorylation kinetics of specific EGFR phosphotyrosines is unknown. The relative rates of EGFR tyrosine dephosphorylation at different cellular locations also remain poorly understood. Beyond this fundamental knowledge gap, there are additional reasons why a quantitative understanding of EGFR tyrosine dephosphorylation is important. Indeed, dephosphorylation rates may influence receptor inhibition by targeted therapeutics (3), receptor trafficking (7), and downstream signaling (8). Tyrosine cycling between phosphorylated and unphosphorylated forms may also influence?receptor sensitivity to noise (9), system responses to?changes in ligand concentration (10), and sensitivity to changes in PTP and receptor concentrations (11). Of course, phosphatases play important roles in regulating signaling downstream of the receptor as well. In linear signaling cascades such as those associated with MAP kinases, phosphatases have a role in regulating signal induction, duration, amplification, and dampening (12). A number of PTPs that regulate EGFR have been identified, including RPTPand has been measured using 125I-EGF, with computed as the slope of internalized 125I-EGF counts versus the integral of surface-bound 125I-EGF counts from computed in this way is not generally interchangeable with the used in model rate equations describing endocytosis of phosphorylated species, even though the constants have similar units. This inconsistency arises because PTP activity at the plasma membrane results in at least some of the ligand-bound, membrane-localized receptors being unphosphorylated. In the limit of vanishing PTP activity at the AZD2171 membrane and rapid dimerization and phosphorylation, values, was iteratively determined for each simulation to achieve agreement between predicted internal and Rabbit Polyclonal to MRPS24. AZD2171 plasma membrane EGF dynamics and a was 1.6-fold larger than (Fig.?2 values increasing the discrepancy between and and plateaus for arbitrarily large values as other processes become rate-limiting. Figure 2 Relationship between model (and was determined for 10?ng/mL EGF and (to allow for steady initial EGFR levels. The and values were determined iteratively before model calculations. Endosomal exit and sorting Receptor exit from the endosome was modeled using previously published parameters (19). The sorting of exiting species for degradation and recycling was modeled by assuming that constant fractions were routed to these pathways (19). EGFR sorting fractions were taken from measurements in mammary epithelial cells (19). Parameter fitting To determine the four unknown parameters, we began by fitting the model to data gathered from parental HeLa cells, AZD2171 including the phosphorylation response of EGFR Y1068 to 1 1 and 10?ng/mL EGF, 100 20?min to minimize potential effects of transcriptional regulation. Because our preliminary analysis revealed that data for the fraction of receptor phosphorylated would constrain parameter estimates more than data for relative changes in phosphorylation alone, we converted our immunoblot data to estimates of the percentage of EGFR phosphorylated at Y1068 (%pEGFR) using immunoprecipitation-based measurements (see Fig.?S2), as described in the Supporting Material. Parameter fitting was accomplished using simulated annealing to minimize the total error between model predictions and experimental data. For most data points, errors were computed as the square of the difference between model prediction and the experimental value divided by the magnitude of the experimental value. For pulse-chase data points, a similar form was used, except that experimental and model values were normalized to their values at 8?min post-EGF. This emphasized the fold-changes in pEGFR signals observed in the pulse-chase experiments. The error associated with each treatment (e.g., 1?ng/mL EGF) was computed as the sum of individual data point errors divided by the number of points for that condition, and the total. AZD2171

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