Malik-Garbi M et al. (2019), Scaling behaviour in steady-state contracting a...

XB-ART-56498

Nat Phys 2019 May 01;155:509-516. doi: 10.1038/s41567-018-0413-4.

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Scaling behaviour in steady-state contracting actomyosin networks.

Malik-Garbi M , Ierushalmi N , Jansen S , Abu-Shah E , Goode BL , Mogilner A , Keren K .

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Contractile actomyosin network flows are crucial for many cellular processes including cell division and motility, morphogenesis and transport. How local remodeling of actin architecture tunes stress production and dissipation and regulates large-scale network flows remains poorly understood. Here, we generate contracting actomyosin networks with rapid turnover in vitro, by encapsulating cytoplasmic Xenopus egg extracts into cell-sized 'water-in-oil' droplets. Within minutes, the networks reach a dynamic steady-state with continuous inward flow. The networks exhibit homogeneous, density-independent contraction for a wide range of physiological conditions, implying that the myosin-generated stress driving contraction and the effective network viscosity have similar density dependence. We further find that the contraction rate is roughly proportional to the network turnover rate, but this relation breaks down in the presence of excessive crosslinking or branching. Our findings suggest that cells use diverse biochemical mechanisms to generate robust, yet tunable, actin flows by regulating two parameters: turnover rate and network geometry.

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	Figure 2. Influence of assembly and disassembly factors on actin network architecture, flow and turnover.Contractile actin networks are generated by encapsulating Xenopus extract supplemented with different assembly and disassembly factors. In all cases, the system reaches a steady-state within minutes. The inward contractile flow and actin network density were measured (as in Fig. 1). (a-c) The steady-state network behavior is shown for samples supplemented with (a) 12.5μM Cofilin, 1.3μM Coronin and 1.3 μM Aip1 (see Movie 2); (b) 1.5μM ActA (see Movie 3); (c) 0.5μM mDia1. For each condition, a spinning disk confocal fluorescence image of the equatorial cross section of the network labeled with GFP-Lifeact (left) is shown, together with graphs depicting the radial network flow and density as a function of distance from the contraction center (middle), and the net actin turnover as a function of network density (right). The thin grey lines depict data from individual droplets, and the thick line is the average over different droplets. The dashed lines show the results for the control unsupplemented sample. (d-g) The concentration-dependent effects of adding ActA (d-f) and mDia1 (g) on network dynamics. (d) The radial network flow is plotted as a function of distance from the contraction center. For each ActA concentration, the mean (line) and std (shaded region) over different droplets are depicted. (e) The network contraction rate in individual droplets is determined from the slope of the fit to the radial network flow as a function of distance. For each ActA concentration, the contraction rate was averaged over different droplets (0μM, N=15; 0.1μM, N=4; 0.5μM, N=3; 0.7μM, N=4; 1.5μM, N=4). To obtain the relative contraction rates, these values were divided by the average contraction rate for the unsupplemented control sample. The relative network contraction rate (mean±std) is plotted as a function of the added ActA concentration. (f) The net actin turnover rate, determined from the divergence of the flux, is plotted as a function of network density for the different ActA concentrations. (g) The relative network contraction rates (mean±std; as in Fig. 2e) are plotted as a function of the added mDia1 concentration. For each mDia1 concentration, the contraction rate was averaged over different droplets (0μM, N=14; 0.1μM, N=12; 0.5μM, N=12; 0.7μM, N=12; 1.5μM, N=10). In (e) and (g), the measured contraction rates in each condition were compared to the control sample (0μM) using the Mann–Whitney test. Conditions for which the contraction rates were statistically different from the control are indicated (*).
	Figure 3. Influence of crosslinking on actin network dynamics.Contractile actin networks are generated by encapsulating Xenopus extract supplemented with different concentrations of the actin crosslinker α-Actinin. The inward contractile flow and actin network density were measured (as in Fig. 1). (a) The steady-state network behavior is shown for a sample supplemented with 10 μM α-Actinin (see Movie 4). A spinning disk confocal fluorescence image of the equatorial cross section of the network labeled with GFP-Lifeact (left) is shown, together with graphs depicting the inward radial network flow and network density as a function of distance from the contraction center (middle) and the net actin turnover as a function of network density (right). The thin grey lines depict data from individual droplets, and the thick line is the average over different droplets. The dashed lines show the results for the control unsupplemented sample. The network contracts in a non-homogeneous manner, reflected by the non-linear dependence of the radial network flow on the distance from the contraction center. (b,c) The concentration-dependent effect of α-Actinin on network density and flow. The network density (b) and radial flow (c) are plotted as a function of distance from the contraction center. For each α-Actinin concentration, the mean (line) and std (shaded region) over different droplets are depicted. The position of the network density peak moves towards the inner boundary with increasing α-Actinin concentrations, and the radial velocity becomes a non-linear function of the distance from the contraction center. (d) The derivative of the radial velocity, −1r2∂∂r(r2V), is plotted as a function of distance from the contraction center. This function becomes position-dependent for α-Actinin concentrations ≥ 4μM. According to the model, this derivative should be approximately equal to the ratio between the active stress and the effective network viscosities, σA(r)2μ(r)+λ(r). (e) The derivative of the radial velocity, −1r2∂∂r(r2V), is plotted as a function of network density. According to the model, this derivative should be approximately equal to the ratio between the active stress and the effective network viscosities, σA(ρ)2μ(ρ)+λ(ρ). For α-Actinin concentrations ≥4μM this ratio becomes density-dependent, indicating that the scaling relation between the active stress and the effective viscosity no longer holds.
	Figure 4. Characteristics of contractile actin networks with turnover.The contracting flows and actin turnover were measured for contractile actin networks formed under different conditions, including 80% extract (control; grey) and samples supplemented with 1.5μM ActA (purple); 2.6μM Fascin (magenta); 0.5μM mDia1 (cyan); 10μM α-Actinin (red); 0.5μM Capping Protein (green); or 12.5μM Cofilin, 1.3μM Coronin and 1.3 μM Aip1 (orange). (a) The radial network flow rate is plotted as a function of distance from the contraction center. For each condition, the mean (line) and std (shaded region) over different droplets are depicted. (b) The net actin turnover as a function of network density is plotted for different conditions. The contraction rate and turnover rate are determined for each droplet from the slopes of linear fits to the radial network flow as a function of distance, and the net turnover as a function of density, respectively (see Methods, Fig. S2). (c) The measured network density profile for the control sample is compared to the predictions of a model which assumes constant turnover and contraction rates (Supp. Information). The measured density distribution (mean and std) nicely matches the model predicted distribution based on the average values of the turnover and contraction rates, β= 1.4 min−1 and k=0.65 min−1 (dashed line). (d) The relative width of the network profile (quantified as the distance between the inner boundary and the position where the network reaches half its maximal value, normalized by the droplet’s radius) is plotted as a function of the ratio between the net turnover rate and the contraction rate. The dots depict values for individual droplets and the error bars show the mean and std for all the droplets examined for each condition. The model results (dashed line; Supp. Information) predict the increase of the network width as a function of the ratio between the net turnover rate and the contraction rate. (e,f) Scatter plots of the contraction rate and net turnover rate for different conditions. For each condition, the dots depict values for individual droplets and the error bars show the mean and std for all the droplets examined for each condition. (e) The contraction rate is correlated with the turnover rate for the conditions examined (Pearson correlation=0.76, p<10−8). The dashed line depicts a linear fit. (f) The correlation between the contraction rate and the turnover rate breaks down for samples with added ActA or α-Actinin. (g) Schematic illustration of the behavior of networks with constant contraction and turnover rates. Increasing the contraction and turnover rates proportionally leads to faster network dynamics but the network density profile remains the same, whereas changes in the ratio between the contraction and turnover rates leads to significant modifications in network structure (Supp. Information).

References [+] :

Abu Shah, Symmetry breaking in reconstituted actin cortices. 2014, Pubmed