H-ras, K-ras, and inner plasma membrane raft proteins operate in nanoclusters with differential dependence on the actin cytoskeleton

Abstract

Plasma membrane compartmentalization imposes lateral segregation on membrane proteins that is important for regulating signal transduction. We use computational modeling of immunogold spatial point patterns on intact plasma membrane sheets to test different models of inner plasma membrane organization. We find compartmentalization at the nanoscale level but show that a classical raft model of preexisting stable domains into which lipid raft proteins partition is incompatible with the spatial point patterns generated by the immunogold labeling of a palmitoylated raft marker protein. Rather, approximately 30% of the raft protein exists in cholesterol-dependent nanoclusters, with approximately 70% distributed as monomers. The cluster/monomer ratio (number of proteins in clusters/number of proteins outside clusters) is independent of expression level. H-rasG12V and K-rasG12V proteins also operate in nanoclusters with fixed cluster/monomer ratios that are independent of expression level. Detailed calibration of the immunogold imaging protocol suggests that radii of raft and RasG12V protein nanoclusters may be as small as 11 and 6 nm, respectively, and shows that the nanoclusters contain small numbers (6.0-7.7) of proteins. Raft nanoclusters do not form if the actin cytoskeleton is disassembled. The formation of K-rasG12V but not H-rasG12V nanoclusters also is actin-dependent. K-rasG12V but not H-rasG12V signaling is abrogated by actin cytoskeleton disassembly, which shows that nanoclustering is critical for Ras function. These findings argue against stable preexisting domains on the inner plasma membrane in favor of dynamic actively regulated nanoclusters similar to those proposed for the outer plasma membrane. RasG12V nanoclusters may facilitate the assembly of essential signal transduction complexes.

Fig. 1.

In classical raft models, a fixed number of rafts accommodate a fixed fraction of raft proteins. As the expression levels of a raft partitioning protein increases (indicated by the arrow) more proteins must be accommodated in the rafts. The extent of clustering of the protein will increase as surface density (λ) increases. The relationship will depend on the extent to which the protein partitions into rafts (expressed here as the clustered fraction, φ, and the total area of the cell surface that is comprised of rafts, μ_A). In an alternative model, the cluster size remains constant, and, as expression increases, more clusters of the same size (indicated by the arrow) form. The key parameters for this model are the mean cluster size (μ_C) and φ. Samples of the point patterns generated by the two models are shown in Fig. 7.

Fig. 2.

Monte Carlo simulations of the raft model described in Fig. 1. (A-C) All combinations of μ_A (in increments of 0.1) and φ (in increments of 0.05) were simulated at three different densities (A, λ = 250 μm^-2; B, λ = 500 μm^-2; C, λ = 1000 μm^-2). For each parameter combination, the mean K function was calculated from 20 simulations. The graphs show the dependence of L_max, the maximal value of the K function, on μ_A and φ. L_max values are plotted as isolines (labeled above and to the right of each chart). The approximate dependence of L_max on λ for any combination of μ_A and φ can be extracted from A-C. (D) Three examples corresponding to •, ▴, and ♦ from A-C are shown.

Fig. 3.

Monte Carlo simulations of the alternative clustering model described in Fig. 1. (A-C) All combinations of μ_C (in increments of 0.1) and φ (in increments of 0.05) were simulated at three different densities (A, λ = 250 μm^-2; B, λ = 500 μm^-2; C, λ = 1,000 μm^-2). For each parameter combination, the mean K function was calculated from 20 simulations. The graphs show the dependence of L_max, the maximal value of the K function, on μ_C and φ. L_max values are plotted as isolines (labeled to the right of each chart). The approximate dependence of L_max on λ for any combination of μ_C and φ can be extracted from A-C. (D) Three examples corresponding to •, ▴, and ♦ from A-C are shown.

Fig. 4.

Surface distributions of GFP-tH and RFP-tH fit a realization of the alternative clustering model but no realization of the classical raft model. (A) Measurements of L_max from 135 individual plasma membrane sheets prepared from BHK cells expressing different levels of GFP-tH (green) or RFP-tH (red); a representative example is shown in Fig. 8, which is published as supporting information on the PNAS web site. (B and C) Two realizations of the raft (B)or alternative clustering (C) models model plotted against the GFP-tH and RFP-tH data sets shown in A: 100 simulations were performed for each λ (over a range of 100-2,000 μm^-2 in increments of 100). The plots show 99% confidence intervals for L_max and the mean value of the simulation.

Fig. 5.

Analysis of a model system to estimate domain size. (A) Schematic representation of the model system showing a 14-nm gold particle coated with GFP and labeled with anti-GFP 4-nm gold. (B) Examples of 14-nm GFP-coated gold particles labeled with anti-GFP 4-nm gold imaged by EM. (C) Patterns comprising large numbers of GFP-coated gold particles labeled with anti-GFP 4-nm gold were imaged by EM using four different sizes of GFP-coated gold particles. The radius of the resulting small gold pattern (the value of r at L_max = r_p) is plotted against the radius of the large gold particles coated with GFP measured directly by EM (r_d). The graph can be used to estimate r_d for the r_p values of 16 nm and 22 nm observed experimentally. (D) The antibody spacer distance (S) can be calculated as S = r_p - r_d (≈10 nm).

Fig. 6.

Actin dependence of plasma membrane nanoclusters. (A-D) Plasma membrane sheets were prepared from cells expressing GFP-tH, H-rasG12V, K-rasG12V, or H-ras that were either untreated or treated for 5 min with 50 nM or 1 μM latrunculin. Sheets were immunogold-labeled, and the gold point patterns were analyzed. Each L(r) - r curve is a mean K function (n = 25-28 replicates), and the total number of gold particles analyzed for each condition was ≥11,372. Differences between control and treatment K functions were evaluated by using a bootstrap test. Treatment with 50 nM or 1 μM latrunculin significantly effected clustering of GFP-tH (P = 0.025 and 0.011, respectively), K-rasG12V (P = 0.047 and 0.015, respectively), and H-ras (P = 0.038). (E) Whole-cell lysates of cells expressing equivalent levels of GFP-K-rasG12V or GFP-H-rasG12V or mock-transfected with empty vector [either untreated (-) or treated (+) with 1 μM latrunculin, respectively] were assayed for ERK activation by using quantitative immunoblotting for phosphorylated ERK (ppERK). A blot representative of three independent experiments is shown.