R-Ras regulates β1-integrin trafficking via effects on membrane ruffling and endocytosis

<p>Abstract</p> <p>Background</p> <p>Integrin-mediated cell adhesion and spreading is dramatically enhanced by activation of the small GTPase, R-Ras. Moreover, R-Ras localizes to the leading edge of migrating cells, and regulates membrane protrusion. The exact mechanisms by which R-Ras regulates integrin function are not fully known. Nor is much known about the spatiotemporal relationship between these two molecules, an understanding of which may provide insight into R-Ras regulation of integrins.</p> <p>Results</p> <p>GFP-R-Ras localized to the plasma membrane, most specifically in membrane ruffles, in Cos-7 cells. GFP-R-Ras was endocytosed from these ruffles, and trafficked via multiple pathways, one of which involved large, acidic vesicles that were positive for Rab11. Cells transfected with a dominant negative form of GFP-R-Ras did not form ruffles, had decreased cell spreading, and contained numerous, non-trafficking small vesicles. Conversely, cells transfected with the constitutively active form of GFP-R-Ras contained a greater number of ruffles and large vesicles compared to wild-type transfected cells. Ruffle formation was inhibited by knock-down of endogenous R-Ras with siRNA, suggesting that activated R-Ras is not just a component of, but also an architect of ruffle formation. Importantly, β₁-integrin co-localized with endogenous R-Ras in ruffles and endocytosed vesicles. Expression of dominant negative R-Ras or knock down of R-Ras by siRNA prevented integrin accumulation into ruffles, impaired endocytosis of β₁-integrin, and decreased β₁-integrin-mediated adhesion. Knock-down of R-Ras also perturbed the dynamics of another membrane-localized protein, GFP-VSVG, suggesting a more global role for R-Ras on membrane dynamics. However, while R-Ras co-internalized with integrins, it did not traffic with VSVG, which instead moved laterally out of ruffles within the plane of the membrane, suggesting multiple levels of regulation of and by R-Ras.</p> <p>Conclusions</p> <p>Our results suggest that integrin function involves integrin trafficking via a cycle of membrane protrusion, ruffling, and endocytosis regulated by R-Ras, providing a novel mechanism by which integrins are linked to R-Ras through control of membrane dynamics.</p

3D Collagen Alignment Limits Protrusions to Enhance Breast Cancer Cell Persistence

Patients with mammographically dense breast tissue have a greatly increased risk of developing breast cancer. Dense breast tissue contains more stromal collagen, which contributes to increased matrix stiffness and alters normal cellular responses. Stromal collagen within and surrounding mammary tumors is frequently aligned and reoriented perpendicular to the tumor boundary. We have shown that aligned collagen predicts poor outcome in breast cancer patients, and postulate this is because it facilitates invasion by providing tracks on which cells migrate out of the tumor. However, the mechanisms by which alignment may promote migration are not understood. Here, we investigated the contribution of matrix stiffness and alignment to cell migration speed and persistence. Mechanical measurements of the stiffness of collagen matrices with varying density and alignment were compared with the results of a 3D microchannel alignment assay to quantify cell migration. We further interpreted the experimental results using a computational model of cell migration. We find that collagen alignment confers an increase in stiffness, but does not increase the speed of migrating cells. Instead, alignment enhances the efficiency of migration by increasing directional persistence and restricting protrusions along aligned fibers, resulting in a greater distance traveled. These results suggest that matrix topography, rather than stiffness, is the dominant feature by which an aligned matrix can enhance invasion through 3D collagen matrices

A Three-Dimensional Computational Model of Collagen Network Mechanics

Extracellular matrix (ECM) strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell- ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model) and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned). We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions

Iterative design and optimization of initially inactive Proteolysis Targeting Chimeras (PROTACs) identify VZ185 as a potent, fast and selective von Hippel-Lindau (VHL)-based dual degrader probe of BRD9 and BRD7

Developing PROTACs to redirect the ubiquitination activity of E3 ligases and potently degrade a target protein within cells can be a lengthy and unpredictable process, and it remains unclear whether any combination of E3 and target might be productive for degradation. We describe a probe-quality degrader for a ligase-target pair deemed unsuitable: the von Hippel-Lindau (VHL) and BRD9, a bromodomain-containing subunit of the SWI/SNF chromatin remodeling complex BAF. VHL-based degraders could be optimized from suboptimal compounds in two rounds by systematically varying conjugation patterns and linkers, and monitoring cellular degradation activities, kinetic profiles, and ubiquitination, as well as ternary complex formation thermodynamics. The emerged structure-activity relationships guided the discovery of VZ185, a potent, fast and selective degrader of BRD9 and of its close homolog BRD7. Our findings qualify a new chemical tool for BRD7/9 knockdown, and provide a roadmap for PROTAC development against seemingly incompatible target-ligase combinations

Simulation of a local deformation test using the calibrated collagen model of 2 mg/ml.

<p>(A) A cubic test box (20 µm×20 µm×20 µm) is located at the center of the simulation box (300 µm×300 µm×300 µm). All beads in the test box are anchored and displaced by 60 µm in the z-direction (black arrow) with a 2 µm displacement step size for 30 steps. Beads in the outer layer of the simulation box (within 50 µm of all the box sides) are anchored. All fiber-beads are initially at equilibrium before the test box is displaced. Average force value was calculated at the quasi-equilibrium state after each displacement step. Average force value of all beads in the test box (B), anchored layer (C), and internal box (D) over 30 displacement steps. Force vectors at the quasi-equilibrium state of 60 µm displacement in the test box (E), anchored layer (F), and internal box (G). Each colorbar shows force scale in the figure. Force histogram at the quasi-equilibrium state of 60 µm displacement in the test box (H), anchored layer (I), and internal box (J). Inset images of figure I and J are magnified views to illustrate the tails of distribution at larger force values.</p

Validation of the best-fit crosslinker parameter values.

<p>(A) Shear modulus of simulation results (Sim) using the best-fit crosslinker parameter values and elastic modulus (G′) in shear experiments (Exp) from Stein et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111896#pone.0111896-Stein1" target="_blank">[30]</a>. 5 independent runs were simulated for seven different collagen densities (1, 1.5, …, 4 mg/ml using a 0.5 mg/ml increment). (B) Tensile modulus of various strain rate experiments, experiments from Provenzano et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111896#pone.0111896-Provenzano3" target="_blank">[41]</a>, Roeder et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111896#pone.0111896-Roeder1" target="_blank">[40]</a>, Riching et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111896#pone.0111896-Riching1" target="_blank">[24]</a>, Lopez-Garcia et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111896#pone.0111896-LopezGarcia1" target="_blank">[42]</a>, predicted values (Pre) using a power-law fitting from Lopez-Garcia et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111896#pone.0111896-LopezGarcia1" target="_blank">[42]</a>, and simulation results using the best-fit crosslinker parameter values. Inset figure is magnified view of our experimental data of 2 mg/ml collagen gels at very slow train rate of 0.046/min.</p

Shear simulation results.

<p>We simulated 8 different crosslinker densities (2, 4, …, 16N, 2N increment), 16 different crosslinker strengths (50, 100, …, 800 KPa, 50 KPa increment), and 4 different collagen densities (1, 2, 3, 4 mg/ml) for random fiber networks, which is total 512 different parameter sets. Shear stress - shear strain curves for ten strains using a 0.01 strain step size are shown in various crosslinker densities with fixed 400 KPa crosslinker strength (A) of 2 mg/ml collagen density case, various crosslinker strengths with fixed 8N crosslinker density (B) of 2 mg/ml collagen density case, and various collagen densities with fixed 400 KPa crosslinker strength and fixed 8N crosslinker density (C). Five independent runs were conducted for each parameter set. Only four curves for each varied parameter are shown for the better visualization. (D) Shear modulus surface plot for four different collagen densities, 8 different crosslinker densities and 16 different crosslinker strength. Each modulus value was calculated from the regression line slope of the stress-strain curve.</p