Research Article: Biophysical Mechanism for Ras-Nanocluster Formation and Signaling in Plasma Membrane

Date Published: July 9, 2009

Publisher: Public Library of Science

Author(s): Thomas Gurry, Ozan Kahramanoğulları, Robert G. Endres, Kumar Selvarajoo.

Abstract: Ras GTPases are lipid-anchored G proteins, which play a fundamental role in cell signaling processes. Electron micrographs of immunogold-labeled Ras have shown that membrane-bound Ras molecules segregate into nanocluster domains. Several models have been developed in attempts to obtain quantitative descriptions of nanocluster formation, but all have relied on assumptions such as a constant, expression-level independent ratio of Ras in clusters to Ras monomers (cluster/monomer ratio). However, this assumption is inconsistent with the law of mass action. Here, we present a biophysical model of Ras clustering based on short-range attraction and long-range repulsion between Ras molecules in the membrane. To test this model, we performed Monte Carlo simulations and compared statistical clustering properties with experimental data. We find that we can recover the experimentally-observed clustering across a range of Ras expression levels, without assuming a constant cluster/monomer ratio or the existence of lipid rafts. In addition, our model makes predictions about the signaling properties of Ras nanoclusters in support of the idea that Ras nanoclusters act as an analog-digital-analog converter for high fidelity signaling.

Partial Text: Plasma membrane heterogeneity is a key concept in molecular cell biology due to its role in protein sorting and specificity of signaling [1]–[3]. Although the diversity of the membrane’s lipid components is partly responsible for this heterogeneity [4], the role played by membrane proteins is less well understood. Members of the Ras protein superfamily [5], [6] have been observed to form dynamic, non-overlapping domains called nanoclusters in the inner leaflet of the plasma membrane [7]–[10]. While the lateral segregation of Ras may provide evidence towards the existence of small, dynamic rafts [11], the definition and even existence of rafts remains disputed [12]. In addition to its connection to the lipid-raft concept, Ras has attracted immense interest due to its fundamental role in a multitude of cellular processes, including cell proliferation, survival, and motility. Most importantly, Ras genes are found to be mutated in 30% of human cancers [13]–[15], making their products extremely important therapeutic targets [16]. While the intracellular biochemistry of Ras genes is well documented, the biophysical mechanism and role of Ras clustering in the plasma membrane remains little understood.

Prior et al.[7] studied Ras clustering in plasma membrane sheets using immunoEM of gold-labeled Ras molecules (Fig. 1A). Gold point patterns were analyzed based on Ripley’s K function. Specifically, the non-linear transformation was applied, where r is the distance between gold particles. This function is zero for complete randomness, positive for clustering, and negative for depletion (Fig. 1B). Plowman et al.[11] used the function’s maximal value, termed for short, as summary statistics for clustering, and found that is independent of Ras expression level (Fig. 2, symbols). This was rationalized by an ad hoc clustering model, assuming a constant cluster/monomer ratio. Analysis of immuno-gold patterns is consistent with small clusters, containing approximately 6 to 8 molecules. Here, we use a biophysical model of Ras clustering in the plasma membrane. In our model, a Ras molecule can be in either an active (on) or an inactive (off) state, corresponding to the respective GTP-bound and GDP-bound molecules for wild-type Ras. Both active and inactive Ras are associated with membrane in line with experimental observation [28]. The equilibrium probability of a single Ras molecule to be active depends on the effective free-energy difference between the on and off states, which in turn depends on input signals. We assume that active Ras molecules experience a short-range attraction, driving cluster formation of active Ras, whereas a long-range repulsion limits cluster size (Fig. 3A, main panel). Such a long-range interaction may result from lipid-anchor induced membrane deformations. To obtain equilibrium properties, we approximate the membrane by a square lattice, populated by Ras molecules of a specified density (Fig. 3A, inset), and perform Monte Carlo simulations. For comparison with immunoEM experiments, we added 10nm-long gold-labeled antibodies (maximally one per Ras) to the Ras molecules in the experimentally observed capture ratio (Fig. 3B). We mainly use the four Ras densities given in Table 1. To specifically compare with experiments on varying Ras-expression level (symbols in Fig. 2, main panel), we calculate the value for additional Ras densities. Note that these experiments are based on the lipid anchor of H-Ras (tH), which has similar clustering properties as active H-Ras [11]. For details on the experiments and our approach, see Methods.

Different Ras isoforms are known to form nonoverlapping signaling clusters [8], [18], important for localized signaling of the Ras-Raf-MEK-ERK pathway [31], [32], involved in cell proliferation, differentiation, and apoptosis [20]. In addition to the fundamental importance of Ras in this pathway, Ras mutations are found in 30% of human cancers [13]–[15]. Ras clusters are also considered evidence of lipid rafts [11]. Lipid rafts have attracted considerable interest due to their alleged role in protein sorting and specificity of signaling [1]–[3]. In this work, we provided a biophysical model of Ras clustering, and compared results with gold-point patterns obtained from immunoEM of plasma membrane extracts (Fig. 1). In particular, we obtained that clustering of Ras molecules, i.e. the cluster/monomer ratio, is independent of expression level (Fig. 2), in line with experiments on the lipid anchor of H-Ras (tH) [11]. In our model, as well as in experiments, clustering is quantified by the maximum value (termed ) of function [11], where r is the distance between gold particles. Our model has two main ingredients exemplified in Fig. 3: (1) a short-range attraction between active Ras molecules ( e.g. Ras GTP) promoting clustering, and (2) a long-range repulsion between Ras molecules, which limits cluster size.