Linear combinations of docking affinities explain quantitative differences in RTK signaling

Andrew Gordus^1,3,a, Jordan A Krall^1,a, Elsa M Beyer^1,a, Alexis Kaushansky¹, Alejandro Wolf-Yadlin¹, Mark Sevecka¹, Bryan H Chang¹, John Rush² & Gavin MacBeath¹

^aThese authors contributed equally to this work

Article highlights

Synopsis

Receptor tyrosine kinases (RTKs) constitute a large family of single-spanning transmembrane proteins found only in Metazoans (Robinson et al, 2000). Their primary role is to mediate intercellular communication by recognizing extracellular ligands and translating that information into an appropriate cellular response (Schlessinger, 2000). Ligand binding to the extracellular domain of an RTK induces receptor dimerization and activation of its intracellular kinase domain, which results in phosphorylation of a number of tyrosine residues in its carboxy-terminal tail. Intracellular signaling proteins that contain Src homology 2 (SH2) or phosphotyrosine-binding (PTB) domains dock at these sites of tyrosine phosphorylation and initiate a variety of signaling cascades within the cell (Sadowski et al, 1986; Kavanaugh and Williams, 1994). Paradoxically, RTKs often use the same signaling pathways to elicit diverse and even opposing phenotypic responses, ranging from adhesion to migration, proliferation to differentiation, and survival to apoptosis (Fambrough et al, 1999; Simon, 2000). The ability of RTKs to signal through common pathways, yet induce diverse phenotypic responses, has largely been attributed to differences in cellular context, as signaling proteins are differentially expressed in different cell types (Jordan et al, 2000; Simon, 2000). When expressed in the same cellular background, however, different RTKs have also been shown to elicit different phenotypic responses (Pollock et al, 1990; Lin et al, 1996). How, then, are intrinsic differences between RTKs manifested within the same cell type, where does the information reside that defines these differences, and how is that information processed?

To address these questions, we expressed six diverse RTKs in the same cellular background (Figure 1A) and monitored their ability to activate downstream signaling pathways. Quantitative immunoblotting was used to measure the relative phosphorylation levels of a wide range of proteins that have previously been implicated in RTK signaling (Figure 1B and C). In total, we queried 65 sites of phosphorylation on 57 proteins and observed growth factor-induced phosphorylation of 24 sites on 23 proteins. Each receptor induced a distinct pattern of phosphorylation, and for every site of phosphorylation, quantitative differences were observed across the six cell lines (Figure 1C). Thus, although these six RTKs activate many of the same signaling proteins, they do so to different quantitative degrees.

Figure 1

Measurement of the intrinsic differences among six receptor tyrosine kinases. (A) The full-length coding regions for six RTKs were introduced into Flp-In-293 cells to generate stable cell lines. Each cell line was serum-starved for 24 h and stimulated for 5 min with a saturating concentration of the indicated growth factor. (B) Cell lysates were analyzed by quantitative immunoblotting to determine the relative levels of 24 phosphorylation sites on 23 signaling proteins across the six cell lines. Representative results are shown for four phosphorylation sites. Error bars indicate the range of biological duplicates. The other 20 bar graphs are provided in Supplementary Figure S2. (C) Heat map illustrating the relative levels of the 24 phosphorylation sites across the six cell lines. The columns of this matrix, Y, constitute relative phosphorylation vectors for each signaling event. (D) Protein microarrays comprising almost every human SH2 and PTB domain were printed in individual wells of 96-well microtiter plates and probed with eight concentrations of each phosphopeptide, ranging from 10 nM to 5 M. Phosphopeptides were derived from established sites of tyrosine phosphorylation on the six RTKs. For each domain–peptide interaction, a saturation-binding curve was obtained and the observed fluorescence, F_obs, was fit to equation (1) to obtain an equilibrium dissociation constant, K_D. (E) K_D values were converted to K_A values (K_D=1/K_A) and each phosphopeptide was represented as a vector of K_A values. (F) Each receptor vector was defined as the sum of its constituent phosphopeptide vectors. The receptor-docking affinity matrix, X, comprises the six receptor vectors. Source data is available for this figure at www.nature.com/msb.

Full figure and legend (656K)Source data for figure 1BC (22K)Source data for figure 1E (125K)Figures & Tables index

As RTKs initiate signaling by recruiting proteins to sites of tyrosine phosphorylation (Schlessinger, 2000), we asked whether there was information in the recruitment properties of the pTyr sites on these receptors that could explain the observed differences in signaling. To address this question, we used protein microarrays to define a quantitative interaction map for each receptor by measuring the affinity of almost every human SH2 and PTB domain for phosphopeptides representing known sites of tyrosine phosphorylation on the six receptors (Figure 1D) (Jones et al, 2006). We found that although there is considerable qualitative overlap in the receptors' recruitment properties, they differ substantially at the quantitative level (Figure 1E and F). We therefore hypothesized that quantitative differences in recruitment potential could explain the observed differences in signaling elicited by each receptor. Using partial least-squares regression, we found that the relative phosphorylation levels of upstream signaling proteins can be accurately predicted using linear models that rely on combinations of receptor-docking affinities, whereas the phosphorylation levels of downstream proteins cannot be predicted using linear models (Figure 3). Additionally, we found that much of the information needed to predict the relative phosphorylation levels of upstream signaling proteins resides in the number and affinity of PI3K- and Shc1-docking sites on the receptor. Interestingly, when we examined the sequences surrounding all known sites of tyrosine phosphorylation on human RTKs as reported in the Phospho.ELM database (Diella et al, 2008), we observed a distinct and significant bias for sites that feature consensus binding sequences for the PTB domain of Shc1 and the SH2 domains of PI3K. This bias is not observed in sites of tyrosine phosphorylation derived from all other human proteins. Thus, we find that intrinsic differences between RTKs are manifested in the degree to which they activate upstream signaling proteins and that much of this information resides in the number and affinity of docking sites for PI3K and Shc1.

Figure 3

Linear combinations of docking affinities can predict upstream, but not downstream, signaling events. PLSR models were tested using leave-one-out cross-validation. (A) Predicted relative phosphorylation levels were plotted as a function of observed relative phosphorylation levels for each signaling event. Representative plots are shown for two upstream events (Stat3 pY705 and CrkL pY207) and two downstream events (Stat3 pS727 and SEK1/MKK4 pS257/pT261). The cross-validated residual, Q², and the P-value are shown for each model. Plots for the other 20 models are provided in Supplementary Figure S6. (B) Q² and P-values for all 24 PLSR models. Q² values below zero were set to zero for display purposes. (C) Q² and P-values for PLSR models built using: (1) the number of docking sites and the docking affinities (K_A); (2) only the number of docking sites (no. of sites); and (3) only the four variables with the highest VIP scores (reduced). Red circles represent upstream signaling events and blue circles represent downstream signaling events.

Full figure and legend (398K)Figures & Tables index

Our results suggest that different RTKs may be able to elicit different phenotypic responses in the same cell type by activating a common set of signaling proteins, but to different quantitative degrees. We propose a model in which information processing by RTKs can be segmented into discrete upstream and downstream layers, and submit that the difficult task of constructing mathematical models of RTK signaling can be parsed into separate problems, with the greatest challenge lying in dissecting the nonlinear layer.

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