Intracellular actin-based transport: how far you go depends on how often you switch

Abstract

Intracellular molecular motor-driven transport is essential for such diverse processes as mitosis, neuronal function, and mitochondrial transport. Whereas there have been in vitro studies of how motors function at the single-molecule level, and in vivo studies of the structure of filamentary networks, studies of how the motors effectively use the networks for transportation have been lacking. We investigate how the combined system of myosin-V motors plus actin filaments is used to transport pigment granules in Xenopus melanophores. Experimentally, we characterize both the actin filament network, and how this transport is altered in response to external signals. We then develop a theoretical formalism to explain these changes. We show that cells regulate transport by controlling how often granules switch from one filament to another, rather than by altering individual motor activity at the single-molecule level, or by relying on structural changes in the network.

Fig. 1.

Particle tracking data and fits for aggregation (70 tracks) and dispersion (40 tracks). For clarity, only every 50th data point is shown in Left and every 20th in Right. The error bars represent errors from the tracking. As described in the text, the solid (blue) lines are fits to simulations, and the dashed (red) lines are fits to the Langevin solution. (Right) A magnification of the aggregation region is shown on to emphasize the fits, errors, and short-time curvature.

Fig. 2.

Fit parameters versus maximum fit time. (A) The Langevin parameter D. (B) The parameter τ. (C) T coefficient A from the power-law fit. (D) The power-law exponent λ. Gray triangles, aggregation; black squares, dispersion. Note that the Langevin parameters have converged, whereas the power-law parameters are not conclusive.

Fig. 3.

Quantification of actin filament network. (A) EM of an aggregating cell; actin is yellow. (B) EM of a dispersing cell. The scale bars (white) are 1 μm long. Platinum replica EM was performed as described (21). (C) Distribution of the number of filaments reachable by a randomly placed cargo during aggregation. (D) The same as in C, during dispersion.

Fig. 4.

Measured probability of finding an AF of a given length. Data are the same for aggregation and dispersion. The mean length is ≈1.3 μm.

Fig. 5.

MFP calculated from simulations at various switching probabilities and touching numbers. Errors reflect both the SD of the separate runs and the error in the measured filament length, as weighted by the probability to get to the end. The dashed lines represent the MFP values from the Langevin formalism; dark (top) is dispersion, and light (bottom) is aggregation. Note that varying density (touching number) alone is insufficient to vary the MFP over the experimentally observed range.

Fig. 6.

Sample simulation data for aggregation (Left) and dispersion (Right). The colored lines represent paths taken by sample walkers and the gray lines represent filaments. (Bar, 10 μm.) All walkers took 1,000 steps or walked for ≈500 s. Note that aggregation paths (Left) tend to be local and clumped, whereas dispersion paths (Right) tend to be spread out and spindly. For aggregation, we set p_s = 50% and n_t = 7.8; and for dispersion, we set p_s = 0% and n_t = 4.2.

Fig. 7.

Displacement histograms from simulations of aggregation and dispersion after 30 s (real time) of diffusion. The small bump in the line near R = 2,500 nm in the dispersion line is from cargos that traveled along only one filament and accounts for <1% of the total probability.