Linear combinations of docking affinities explain quantitative differences in RTK signaling

Receptor tyrosine kinases (RTKs) process extracellular cues by activating a broad array of signaling proteins. Paradoxically, they often use the same proteins to elicit diverse and even opposing phenotypic responses. Binary, ‘on–off' wiring diagrams are therefore inadequate to explain their differences. Here, we show that when six diverse RTKs are placed in the same cellular background, they activate many of the same proteins, but to different quantitative degrees. Additionally, we find that the relative phosphorylation levels of upstream signaling proteins can be accurately predicted using linear models that rely on combinations of receptor-docking affinities and that the docking sites for phosphoinositide 3-kinase (PI3K) and Shc1 provide much of the predictive information. In contrast, we find that the phosphorylation levels of downstream proteins cannot be predicted using linear models. Taken together, these results show that information processing by RTKs can be segmented into discrete upstream and downstream steps, suggesting that the challenging task of constructing mathematical models of RTK signaling can be parsed into separate and more manageable layers

High- and Low-Affinity Epidermal Growth Factor Receptor-Ligand Interactions Activate Distinct Signaling Pathways

Signaling mediated by the Epidermal Growth Factor Receptor (EGFR) is crucial in normal development, and aberrant EGFR signaling has been implicated in a wide variety of cancers. Here we find that the high- and low-affinity interactions between EGFR and its ligands activate different signaling pathways. While high-affinity ligand binding is sufficient for activation of most canonical signaling pathways, low-affinity binding is required for the activation of the Signal transducers and activators of transcription (Stats) and Phospholipase C-gamma 1 (PLCγ1). As the Stat proteins are involved in many cellular responses including proliferation, migration and apoptosis, these results assign a function to low-affinity interactions that has been omitted from computational models of EGFR signaling. The existence of receptors with distinct signaling properties provides a way for EGFR to respond to different concentrations of the same ligand in qualitatively different ways

Stat proteins and PLCγ1 cannot be activated by low EGF concentrations in A431 cells.

<p>Serum-starved A431 cells were treated with either 1 nM or 32 nM EGF. Phosphorylation of EGFR and downstream signaling proteins was monitored over the course of 30 minutes by quantitative immunoblotting. PLCγ1 and the Stat proteins were only activated at the high concentration of EGF (32 nM). All other signaling proteins were activated at both high (32 nM) and low (1 nM) concentrations of EGF. Phosphorylation levels were scaled relative to the maximum signal observed for each antibody. Error bars indicate the range of two biological replicates.</p

Distinct subsets of signaling proteins are activated by different concentrations of EGF.

<p><i>A</i>–<i>E.</i> Serum-starved A431 cells were treated for five minutes with different concentrations of EGF, ranging from 250 pM to 32 nM. Phosphorylation levels were determined by immunoblotting with phosphospecific antibodies and scaled relative to the maximum level observed for each antibody. <i>A</i>. All 12 signaling proteins, as well as two sites of phosphorylation on EGFR. Error bars indicate the standard error of the mean (SEM) of three biological replicates. Representative immunoblots are shown for each antibody. <i>B–D.</i> Proteins shown in panel <i>A</i> were divided into three subsets. EGFR tyrosine phosphorylation is shown in each plot for comparison. Error bars have been omitted for clarity. <i>B</i>. Proteins that are phosphorylated at low concentrations of EGF. <i>C</i>. Proteins that require high concentrations of EGF to be phosphorylated. <i>D</i>. Proteins with atypical responses. <i>E</i>. A subset of the data from panel <i>A</i> is shown, highlighting the lowest concentrations of EGF. <i>F.</i> Serum-starved A431 cells were treated for five minutes with different concentrations of EGF, ranging from 31 pM to 32 nM. Phosphorylation levels were plotted on a log scale to illustrate responses at low EGF concentrations. <i>G</i>. A saturation-binding curve (inset) was generated for EGF binding to A431 cells. Bound EGF is scaled relative to maximum binding. A Scatchard plot of EGF binding to A431 cells was generated by plotting the ratio of bound-to-free EGF as a function of bound EGF. Error bars indicate the SEM of five biological replicates.</p

Activation of low-affinity EGFR alters cellular adhesion properties.

<p>Phase-contrast images of A431 cells treated with different concentrations of EGF for 12 hours. <i>A.</i> Serum-starved cells. <i>B.</i> Cells grown in 10% serum. Onset of the cell clumping phenotype coincides with Stat phosphorylation.</p

Distinct subsets of signaling proteins are activated by different concentrations of both EGF and TGFα in multiple cell lines.

<p>Serum-starved cells were treated for five minutes with different concentrations of EGF or TGFα, ranging from 250 pM to 32 nM. Phosphorylation levels were determined by immunoblotting with phosphospecific antibodies and scaled relative to the maximum level observed for each antibody.</p

High and low concentrations of EGF induce distinct phenotypic outcomes.

<p><i>A</i>. Serum-starved A431 cells were treated with 0 nM, 0.5 nM or 16 nM EGF. At the indicated times, cells were trypsinized and counted. Error bars represent the SEM of three biological replicates. <i>B.</i> Left, A431 cells were serum-starved for 24 hours and treated with different concentrations of EGF for 12 hours. BrdU was added to the culture medium for the last hour of the EGF incubation. Cell proliferation was recorded as the change in the percentage of BrdU-positive cells relative to unstimulated cells (no EGF). Error bars represent the SEM of three biological replicates. Right, the cell proliferation data overlaid with the relative phosphorylation levels of Erk and Stat1 as reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015945#pone-0015945-g001" target="_blank">Fig. 1</a>. Error bars have been omitted for clarity. The decrease in proliferation coincides with the increase in Stat1 phosphorylation. <i>C.</i> Left, A431 cells grown in 10% serum were treated with different concentrations of EGF for 24 hours and BrdU incorporation was determined as in <i>B</i>. Right, the cell proliferation data overlaid with the relative phosphorylation levels of Erk and Stat1 in these cells. Error bars have been omitted for clarity.</p

Heavy-atom tunneling in semibullvalenes

The Cope rearrangement of selectively deuterated isotopomers of 1,5-dimethylsemibullvalene $\bf {2 a}$ and 3,7-dicyano-1,5-dimethylsemibullvalene $\bf {2 b}$ were studied in cryogenic matrices. In both semibullvalenes the Cope rearrangement is governed by heavy-atom tunneling. The driving force for the rearrangements is the small difference in the zero-point vibrational energies of the isotopomers. To evaluate the effect of the driving force on the tunneling probability in $\bf {2 a}$ and $\bf {2 b}$, two different pairs of isotopomers were studied for each of the semibullvalenes. The reaction rates for the rearrangement of $\bf {2 b}$ in cryogenic matrices were found to be smaller than the ones of $\bf {2 a}$ under similar conditions, whereas differences in the driving force do not influence the rates. Small curvature tunneling (SCT) calculations suggest that the reduced tunneling rate of $\bf {2 b}$ compared to that of $\bf {2 a}$ results from a change in the shape of the potential energy barrier. The tunneling probability of the semibullvalenes strongly depends on the matrix environment; however, for $\bf {2 a}$ in a qualitatively different way than for $\bf {2 b}$

Prediction of human population responses to toxic compounds by a collaborative competition

\u3cp\u3eThe ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r &lt; 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r &lt; 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.\u3c/p\u3