Membrane interactions of the globular domain and the hypervariable region of KRAS4b define its unique diffusion behavior

RAS is an oncoprotein that functions as a molecular switch at the apex of a signaling network (Stephen et al., 2014) regulating cell differentiation and sustaining cell proliferation, survival, and migration. In oncogenic mutants, this molecular switch mechanism is damaged, and RAS becomes constitutively active—or locked in a GTP-loaded state. In this active state, RAS is in a conformation that favors interaction with several effectors, including RAF, a serine/threonine kinase that, when complexed with RAS at the membrane, phosphorylates its substrate MEK and initiates the mitogenic-activated protein kinase signaling cascade (Moodie et al., 1993; Van Aelst et al., 1993; Warne et al., 1993). RAS biology is characterized by its dynamic association with the plasma membrane. The protein’s lateral mobility, nanoclustering of both RAS and lipid species, and interactions between specific lipid species and amino acid residues in the C-terminal hypervariable region (HVR) are all necessary for its function; however, precisely how this is regulated, and in what order events occur has not been fully characterized (Murakoshi et al., 2004)((Lommerse et al., 2005; Nan et al., 2015; Zhou et al., 2017).

RAS proteins associate with the plasma membrane (PM) through the HVR. These 22 to 23 amino acids mediate interactions with the lipid bilayer and contain key residues that are post-translationally modified (Ahearn et al., 2012). All RAS isoforms contain a C-terminal cysteine residue that is farnesylated and carboxymethylated. HRAS, NRAS, and KRAS4a isoforms contain one or two additional cysteine residues that can be reversibly palmitoylated (Figure 1a). In contrast to the other isoforms, KRAS4b is not palmitoylated, but contains a series of six adjacent charged lysine residues (Figure 1a). These lysines carry positive charges at a neutral pHand favor electrostatic interactions with anionic lipids, present in cellular membranes enriched with phosphatidylserine and other negatively charged lipids (Ahearn et al., 2012). These electrostatic interactions facilitate lipid-based partitioning and sorting of KRAS4b into the disordered domain of membrane (Zhou et al., 2017).

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Figure 1—source data 1

MSD values over time (plotted in Figure 1c) for Halotag-KRAS4b, -KRAS4a, -HRAS, and -NRAS transiently expressed in HeLa cells and recorded with TIRF microscopy.

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Figure 1—source data 2

Diffusion coefficients and occupancy fractions obtained by HMM analysis (plotted in Figure 1d) of Halotag-KRAS4b, -KRAS4a, -HRAS, and -NRAS transiently expressed in HeLa cells.

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Figure 1—source data 3

MSD values over time (plotted in Figure 1e) for Halotag-KRAS4b, -KRAS4a, -HRAS, and -NRAS expressed in isogenic Mouse Embryonic Fibroblast cell pools.

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Figure 1—source data 4

Diffusion coefficients and occupancy fractions obtained by HMM analysis (plotted in Figure 1f) of Halotag-KRAS4b, -KRAS4a, -HRAS, and -NRAS expressed in isogenic Mouse Embryonic Fibroblasts.

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Figure 1—source data 5

MSD values over time (plotted in Figure 1g) for Halotag-KRAS4b, Halotag-KRAS4b HVR (lacking the G domain), Halotag-HRAS, and Halotag-HRAS HVR transiently expressed in HeLa cells.

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Figure 1—source data 6

Diffusion coefficients and occupancy fractions obtained by HMM analysis (plotted in Figure 1h) of Halotag-KRAS4b HVR and Halotag-HRAS HVR transiently expressed in HeLa cells.

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The PM is a highly heterogeneous organelle composed of approximately half lipid and half protein by mass (Cooper, 2000). The physical and chemical properties of the lipids, proteins, and associated actin cytoskeleton create a multi-tiered, hierarchical 2D structure where molecules diffuse between compartments with length scales on the order of hundreds of nanometers and encounter subdomains with smaller dimensions (Kusumi et al., 2012). Diffusing molecules such as RAS traverse these hierarchical, non-equilibrated environments, which create highly transitory, reversible and dynamic subdomains. Using single molecule FRET experiments, Marakoski and colleagues showed that the mobility of membrane-bound KRAS and HRAS was reduced upon growth factor stimulation (Murakoshi et al., 2004). This suggests that activated or oncogenic RAS molecules become more confined (Lommerse et al., 2005; Murakoshi et al., 2004). These investigators have proposed models where oncogenic and activated RAS associate in signaling complexes, perhaps in association with scaffolding molecules and the actin cytoskeleton.

In this study, we use single molecule tracking to detect the mobility of RAS in living cells (Figure 1—figure supplement 1a). Using advanced optical microscopy and image analysis techniques combined with engineered cell lines expressing a variety of constructs, we characterize and dissect the relative contributions of the G-domain and the HVR region of KRAS4b to its mobility and compare it to the mobility of other RAS isoforms (Figure 1a). We observed that KRAS4b has more confined diffusion in the cell membrane compared to other RAS isoforms, and a unique mobility pattern that is best fit by a three-component model composed of fast, intermediate, and relatively immobile states (Figure 1b). By assembling a data set with thousands of trajectories from each cell, we observe transitions between these states; and consequently, we can quantify the probabilities that single molecules of RAS will change states. Intriguingly, we observe that these transitions follow a pattern that is suggestive of a molecular assembly process. We hypothesize that these states are dependent on both GTP-bound G-domain interactions and electrostatic interactions of KRAS4b’s unique, highly charged HVR. To develop a molecularly detailed understanding, we use atomistic molecular dynamics simulations to probe the relative contributions of these domains. The results of these simulations are well correlated to our experimental results and provided further testable hypotheses that we explore in cell experiments. We propose a model where on tethering to the membrane via its farnesyl tail, KRAS4b molecules explore the membrane lipid environment in a fast-moving state. While exploring the membrane environment, negatively charged lipids cluster around the lysines in the HVR of KRAS4b forming a nanodomain. When KRAS4b molecules are GTP-loaded within these nanoclusters, KRAS4b can interact with effectors and transition to slower moving states where it can further assemble with effectors and lipids to form a relatively immobile and confined complex competent to signal downstream.

RAS is primarily associated with the inner leaflet of the cell plasma membrane and membrane association is critical to its activity (Simanshu et al., 2017). One distinct feature of all RAS isoforms lies in the sequence specificity of their HVRs, which may influence where in the plasma membrane RAS molecules associate or may itself influence the assembly of lipid domains through the recruitment of lipids into nanoclusters. RAS shows a high degree of lateral mobility in the PM, and the PM is heterogenous and contains distinct lipid domains that may influence RAS and cell signaling (Kusumi et al., 2012). Understanding how these dynamics are associated with RAS activity, and whether RAS plays a role in regulating these interactions, is a key focus of this work. In this study, we tested the hypothesis that the unique diffusion characteristics of KRAS4b are due to features of the full-length protein, and that these features lead to unique interactions with the PM and to an assembly process of the signaling complex.

KRAS4b shows mobility characterized by three states: a fast, intermediate and slow diffusion rate. The transition probabilities show that the transition between slow and fast states is rare; specifically, the transitions occur through the intermediate state. In light of these findings, we propose a model (Figure 6d) in which the fast state represents RAS molecules that are not complexed with effector molecules and are diffusing with lipid molecules, and in which the two slower states represent different points in the assembly process—the intermediate state representing slowing, partially complexed, RAS molecules, and the slow state representing fully assembled RAS molecules in a signaling complex (that might be highly constrained by unique structural features in the plasma membrane such as a actin corral as proposed by Kusumi et al., 2012). In support of our model, we can control the fractional occupancy of these states by either serum starving KRAS4b-dependent MEFS, which increases the fast state occupancy, or stimulating starved cells with serum, which increases the number of molecules transitioning to the intermediate and slow states. This along with the mutational analysis we collected in HeLa cells, is strong evidence that transition to the slower states of KRAS4b is dependent on G-domain interactions.

We used simulations of KRAS4b and the 4bHVR in defined lipid environments to observe in atomistic detail the correlation of amino acid and lipid interactions and distance measurements and compared them to our experimental data. Computer simulations predict that the positive charges on the HVR recruit negatively charged lipids and bring the HVR into closer proximity to lipids in the membrane. Correspondingly, in experiments if we neutralize or reverse the positive charges in the HVR, we see increasing diffusion rates in the fast component, suggesting that the recruitment of negatively charged lipids is responsible for the fast component’s diffusion rate (0.97 µm²/s ± 0.03). Although we do not directly observe the recruitment of negatively charged lipid molecules around the HVR in experimental data (as this is beyond our imaging capabilities), the atomistic MD simulations clearly show this to be the case.

Furthermore, the MSD data gives direct information about KRAS4b mobility and allows us to characterize the qualitative features of RAS diffusion; specifically, the extent to which the molecules show diffusion patterns of free, anomalous, or more constrained diffusion. Using MSD analysis (without any modeling assumptions), we show that KRAS4b shows distinctively confined diffusion behavior compared to the other isoforms or truncation mutants. For membrane-associated molecules, such as lipids and proteins, anomalous diffusion is thought to be caused by molecular crowding, or confinement to specific subdomains in the lipid bilayer. However, we demonstrate that the confinement of KRAS4b is dependent on the G-domain, since the truncated mutant HaloTag-4bHVR shows almost free diffusion. We suspected that growth factor driven signaling followed by molecular assembly among effector and KRAS4b, and other proteins might be responsible for slowing down the molecular diffusion. Indeed, serum-starved MEF cells expressing the full-length protein, show reduced confinement. When we rescue cells with complete, serum containing media, the KRAS4b molecules change their diffusion behavior and become more confined, as indicated by the bend in the MSD plots (Figure 6a). Furthermore, oncogenic KRAS4b-Q61R, which is constitutively loaded with GTP, shows more confined behavior than KRAS4b molecules that express a mutation in the switch one binding domain (Y40C), which abrogates RAF engagement with RAS. Therefore, effector binding, and signaling in the cells plays a role in confinement of KRAS4b molecules. Finally, we extended the study using atomistic molecular simulations where we show that specific residues in the G-domain form transient contacts with lipids in the membrane. In follow-up biological experiments, we mutated these residues in KRAS4b to be HRAS-like and NRAS-like, and we saw less confined diffusion for these RAS molecules.

It is important to note that diffusion in the membrane is not linearly dependent on the size of the diffusing species (Saffman and Delbrück, 1975); however, these findings suggest that it is both protein-protein and lipid-G-domain interactions that are influencing KRAS4b mobility in ways that are distinct from other isoforms of RAS. We propose in our model that the positive charges on the 4bHVR recruit and cluster negatively charged lipids around RAS molecules (mutation of the HVR lysines leads to a higher diffusion rate, suggesting that these lipid associations contribute to the fast diffusion component), and subsequent G-domain interactions with both effectors and lipids lead to an ordered, stepwise assembly process where KRAS4b molecules are increasingly confined to smaller nanodomains in the formation of a signaling complex (Figure 6c).

Our study clarifies several unresolved issues in the field. Murakoshi et al. (2004) used a single molecule fluorescence energy transfer (FRET) technique to observe single molecule activation of RAS molecules in the plasma membrane of cells. Using a fluorescently labeled GTP analogue which they microinjected into cells and exogenously expressed fluorescently tagged RAS fusion proteins, they observed single molecule trajectories of activated RAS molecules loaded with the fluorescent GTP molecule and reported slow diffusion rates for the active RAS molecules. Lommerse et al. (2005) focused their studies on HRAS. They compared wild type, constitutively active (G12V) and inactive (S17N) forms of HRAS and found that both mutants had two populations of either slow or fast diffusing molecules. In the case of the G12V mutant, they found that the slow-moving molecules were confined to small 200 nm domains, and that wild type proteins were confined to domains of the same size upon insulin stimulation. Consistent with our observations, both studies found that these trajectories were suppressed or immobilized upon activation of the RAS molecule. Although these studies elegantly demonstrate that activation of RAS molecules with GTP loading or oncogenic activation changes diffusion properties, we were able to extend these observations and show differences between isoforms and provide further insight to identify both G-domain and HVR contributions to this mobility pattern.

Zhou et al. (2017) showed that the amino acid sequence of the HVR of KRAS and the prenyl group function as a code for determining the nature of RAS associated nanoclusters. Using a combination of electron microscopy, spatial mapping, and atomistic simulations, their study suggests that HVR interactions are not just electrostatic in nature, but that specific amino acid side chains direct which types of lipids will cluster in the plasma membrane, and that this may have implications in downstream signaling events. Our studies complement these observations by revealing the dynamics of KRAS4b and that mutations in HVR lysines change the diffusion behavior, indicating that the assembly of signaling complexes may be dependent on the nature of the lipid clusters forming around the HVR side chains. We add an additional level of detail, showing by atomistic molecular dynamic simulations that G-domain residues are forming transitory contacts with lipid, and that mutation in these residues affect diffusion behavior. Interestingly, when we convert these G-domain residues of KRAS4b to mimic HRAS and NRAS, the diffusion properties of the molecule show less KRAS4b-like confined behavior.

In summary, we show that the diffusion of KRAS4b in the PM is best explained by a three-state model in which a slow (immobile) state involves G-domain interactions with both the PM and effector engagement. Although we did not focus our efforts on characterizing them, the other RAS isoforms clearly undergo different mechanisms of dynamic interaction with the plasma membrane, as indicated by both their relative lack of confinement (MSD) and fewer states (vbSPT). Our findings strongly suggest that the KRAS4b-plasma membrane interaction plays an important role in regulating RAS signaling through a stepwise and ordered process that gives rise to a specific transition path with different diffusion properties. Furthermore, our study has implications for understanding the interaction of proteins with the plasma membrane, and the active processes involved in assembly of a signaling complex that involves both protein-lipid and protein-protein interactions.

Trajectories and inputsfor the molecular dynamic simulations, have been provided on the webpage at https://bbs.llnl.gov/data.html. Imaging data has been uploaded to Zenodo:http://doi.org/10.5281/zenodo.3697985.

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   Membrane interactions of the globular domain and the hypervariable region of KRAS4b define its unique diffusion behavior.

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Author details

1. Debanjan Goswami

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology

   Contributed equally with

   De Chen

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5910-3811

2. De Chen

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology

   Contributed equally with

   Debanjan Goswami

   Competing interests

   No competing interests declared

3. Yue Yang

   Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, United States

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

4. Prabhakar R Gudla

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. John Columbus

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Resources, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Karen Worthy

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Validation, Investigation, Visualization

   Competing interests

   No competing interests declared

7. Megan Rigby

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Formal analysis, Validation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

8. Madeline Wheeler

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Formal analysis, Validation, Writing - review and editing

   Competing interests

   No competing interests declared

9. Suman Mukhopadhyay

   NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

10. Katie Powell

    NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

    Contribution

    Resources, Validation, Investigation

    Competing interests

    No competing interests declared

11. William Burgan

    NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

    Contribution

    Resources, Investigation, Writing - review and editing

    Competing interests

    No competing interests declared

12. Vanessa Wall

    NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

    Contribution

    Resources, Formal analysis, Investigation

    Competing interests

    No competing interests declared

13. Dominic Esposito

    NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

14. Dhirendra K Simanshu

    NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

    Contribution

    Conceptualization, Investigation, Visualization, Writing - review and editing

    Competing interests

    No competing interests declared

15. Felice C Lightstone

    Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, United States

    Contribution

    Conceptualization, Formal analysis, Supervision

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1465-426X

16. Dwight V Nissley

    NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

    Contribution

    Conceptualization, Supervision

    Competing interests

    No competing interests declared

17. Frank McCormick

    UCSF Helen Diller Family Comprehensive Cancer Center, School of Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

18. Thomas Turbyville

    NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, United States

    Contribution

    Conceptualization, Supervision, Methodology, Project administration

    For correspondence

    turbyvillet@mail.nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2638-9520

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