Signaling from the Living Plasma Membrane Hernán E. Grecco, Malte Schmick, Philippe I.H. Bastiaens  Cell  Volume 144, Issue 6, Pages 897-909 (March 2011) DOI: 10.1016/j.cell.2011.01.029 Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 1 Mechanisms Regulating Lateral Signaling across the Plasma Membrane (A) The reactivity of the plasma membrane is modulated by spatial constraints. Schematic depiction of receptor tyrosine kinase density in cytoskeleton-mediated membrane domains (top) and corresponding distributions (bottom) for different amounts of receptor per cell (N). (B) A bistable system generated by a receptor tyrosine kinase that inhibits its own inhibitory protein tyrosine phosphatase. Two-dimensional time evolution of membrane-bound receptor activation after an initial local point activation shows spreading of the signal. (C) A Turing system generated by a receptor tyrosine kinase that activates its own inhibitory protein tyrosine phosphatase. Two-dimensional time evolution after global stimulation shows the generation of kinase activity hot spots in the plasma membrane. Cell 2011 144, 897-909DOI: (10.1016/j.cell.2011.01.029) Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 2 H2O2-Dependent Regulation of Signal Penetration in the Cytoplasm (A) Schematic representation of a double-negative kinase phosphatase feedback system that regulates substrate phosphorylation. Receptor tyrosine kinases (RTKs), their phosphatases (PTPs), and their substrates (S), such as PRXI, are present in two states. Conversion between states is denoted by curved arrows; mediation of conversion, straight arrows. Phosphorylated species are denoted by subscript p; active species, subscript a; inactive, subscript i. In case of PRXI, the phosphorylated state is the inactive state. Phosphorylation of the receptor mediates extracellular H2O2 production via recruited NOX, and active PRXI reduces H2O2. (B) Computer simulation of diffusion and reaction of substances as detailed in (A) with a cellular automaton approach. Simulations were performed in the presence (left) or absence (right) of PRXI regulation. Lateral and axial concentration profile 50 ms (first row) and 170 ms (second row) after ligand binding to a receptor tyrosine kinase at the membrane, separating the cytosol from the extracellular domain (black). (Third row) Lateral membrane receptor phosphorylation levels. Coloring indicates the propagation in time of the high levels of phosphorylated receptor across the membrane. The penetration of phosphorylated substrate Sp is only marginally affected when PRXI is regulated. However, the lateral speed of membrane signal propagation is increased due to increased receptor reactivity. Parameters were chosen in accordance with the literature, as available, and to preserve bistability of activation of RTK in the membrane (Reynolds et al., 2003). Specifically: Dreceptor = 0.2 μm2/s; Dsubstrate = 10 μm2/s; DROS = 100 μm2/s; kPTP = 25 s−1; kRTK = 1 s−1; kNOX = 10 s−1; kPRXIa = 100 s−1; kROS = 5 s−1. (C) Axial concentration profile of Sp, H2O2, PTPp, and PRXIp. Blue curves depict regulation of PRXI by RTK/PTP; green curves are in absence of PRXI regulation, as in (B); red curves are in absence of PTP inactivation via H2O2 to demonstrate the high impact of PTP regulation on the extent of substrate phosphorylation. Cell 2011 144, 897-909DOI: (10.1016/j.cell.2011.01.029) Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 3 Constraining Receptor Diffusion Accelerates Activation (A) Example of Brownian motion simulations for a receptor with a single (top) or double (bottom) step activation mechanism. A plot shows the time evolution (x axis) for 20 receptors (y axis) for a particular realization of the simulation in which a single receptor is activated at time 0. The receptor state is color coded. Three key frames depicting the receptor's trajectory for highlighted particles are shown. (B) Average (solid line) evolution of the fraction of receptor in each state for 100 realizations of the simulation. The standard error of the mean is indicated (dashed lines). t1 and t2 indicate the times at which the activated population reaches 50%. (C) The simulation was repeated for different domain sizes and numbers of particles. The difference between t2 and t1 is reduced as the size of the domain is reduced. Cell 2011 144, 897-909DOI: (10.1016/j.cell.2011.01.029) Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 4 Localization of RAS through the Acylation Cycle (A) To create a full three-dimensional (3D) simulation of the reaction-diffusion system that underlies the acylation cycle, a 3D stack of confocal images (left) is registered to identify three compartments (right): plasma membrane (white), cytosol/endoplasmic reticulum (gray), and Golgi (red). Computer simulations were performed with a cellular automaton approach to reflect: palmitoylated versus unpalmitoylated RAS; localized PAT-activity at the Golgi; ubiquitous thioesterase activity; unidirectional transport of palmitoylated species from Golgi to the plasma membrane; high inter- and intracompartmental mobility of unpalmitoylated versus low mobility of palmitoylated species. (B) Localization of the two species of palmitoylated (green) versus unpalmitoylated RAS (red) in presence of ubiquitous thioesterase activity (top row) and blocking thioesterase activity (bottom row) shown as an overlay of both species colored according to the 2D color map (right), wherein white denotes oversaturation of palmitoylated RAS (otherwise green). Starting from the initial condition of 100% unpalmitoylated RAS (left), the distribution of palmitoylation evolves toward enrichment of palmitoylated RAS at the plasma membrane and Golgi (upper-right) versus unspecific distribution over all membranes in case of thioesterase inhibition (lower-right). Cell 2011 144, 897-909DOI: (10.1016/j.cell.2011.01.029) Copyright © 2011 Elsevier Inc. Terms and Conditions