Bidirectional cargo transport: moving beyond tug of war

Abstract

Vesicles, organelles and other intracellular cargo are transported by kinesin and dynein motors, which move in opposite directions along microtubules. This bidirectional cargo movement is frequently described as a 'tug of war' between oppositely directed molecular motors attached to the same cargo. However, although many experimental and modelling studies support the tug-of-war paradigm, numerous knockout and inhibition studies in various systems have found that inhibiting one motor leads to diminished motility in both directions, which is a 'paradox of co-dependence' that challenges the paradigm. In an effort to resolve this paradox, three classes of bidirectional transport models--microtubule tethering, mechanical activation and steric disinhibition--are proposed, and a general mathematical modelling framework for bidirectional cargo transport is put forward to guide future experiments.

Figure 1. Three classes of bidirectional transport in cells

A: Neurons contain numerous membrane-bound vesicles that are transported bidirectionally by kinesin and dynein motors in both axons and dendrites. B: In many diverse cell types, larger organelles such as mitochondria, melanosomes, peroxisomes, and lipid droplets are transported bidirectionally along microtubules. C: Intraflagellar transport (IFT) involves the bidirectional transport of proteins (IFT particles) along axonemal microtubules in cilia and flagella by kinesin-2 (KIF3) and dynein-2 (IFT dynein) motors that are attached to the cargo.

Figure 2. The tug-of-war model

A: Schematic of bidirectional transport showing cargo with bound dynein motors that move to the minus-ends of microtubules (retrograde transport) and kinesin motors that move to the plus-ends of microtubules (anterograde transport). Cargo move bidirectionally with runs interrupted by periodic pauses. The bottom plot is a sketch of a typical kymograph, which is created by stacking line-scans from successive frames in a movie of bidirectional transport. Shallow lines denote fast movement and vertical lines denote no movement. B: Experimental observations of bidirectional transport. In this experiment by Hendricks and colleagues, bidirectional transport of neuronal vesicles was visualized both by live-cell imaging in neurons and by in vitro reconstitution on immobilized microtubules. (Top) Montage of six images taken over 10 seconds and the resulting kymograph (far right) showing the bidirectional transport of Lyso-Tracker-labeled vesicles in neurons. While the upper vesicle takes long runs in both directions, the lower vesicle shows only small fluctuations. (Bottom) Similar results are seen for purified vesicles containing dynactin-GFP moving along immobilized microtubules in vitro. Image of rhodamine-labeled microtubules is shown at left, followed by a montage of five images taken over 11 seconds, and the resulting kymograph. C: Results from simulations of a theoretical tug-of-war model. Simulations by Müller and colleagues show that model-generated bidirectional transport dynamics can recapitulate experimental data and that the specific transport characteristics depend strongly on the choice of parameters and numbers of motors used. In these simulations, six kinesin and six dynein motors were modeled, with the motor parameters estimated from lipid droplet experiments. Simulation A2 used the default parameters and show robust runs in both directions analogous to those observed experimentally. In B2 the dynein stall force was reduced by a factor of 2.4 and the dynein dissociation rate constant was reduced by 12%, resulting in net plus-end movement with occasional stalls. In C2, the dynein stall force was also reduced by a factor of 2.4, but the dynein dissociation rate constant was increased by a factor of 2, resulting in smooth plus-end movement (dominated by kinesin).

Figure 3. Different mechanisms for the “pause” state

A: In the “Draw” state, both motors are engaged and stalled. The resulting static position of the cargo in the pause state is represented as a line at position of zero in the corresponding plot. B: In the “Diffusive” state, both motors are detached and cargo is diffusing. The corresponding plot shows 1D diffusion of three simulated cargo, each having a diffusion constant 0.05 µm²/s. As can be seen, diffusion can result in substantial excursions of the cargo in both directions. C: In the “Kicking and Screaming” state, both motors are engaged and moving slowly due to their inherently slow reverse walking speeds. The plot shows results from a stochastic simulation of tug-of-war between 1 kinesin-1 family member and 7 dyneins using the modeling framework and kinesin-1 and dynein parameters from Müller et al.. Three 20 second periods of minimal displacement are shown, highlighting that although the displacements are generated by a different mechanism, they can appear qualitatively similar to the excursions seen in the Diffusive state.

Figure 4. Examples of antagonistic motor codependence

A: The anterograde motor kinesin was inhibited in mouse neurons by knocking out kinesin light-chain 1 (KLC1−/−) or by knocking down light-chain 2 (KLC2 shRNA), both of which are involved in linking kinesin-1 to PrP^c vesicles. Vesicles were labeled by YFP-PrP^c and their transport dynamics analyzed by kymographs. The wild-type kymograph (top) shows left and right diagonal tracks, indicative of retrograde and anterograde cargo transport, respectively, as well as some vertical lines indicative of stationary vesicles. In contrast, the kinesin inhibition kymographs (middle and bottom) show very little transport in either the retrograde or anterograde directions, and many stationary vesicles. Schematic plots of the data demonstrate that while kinesin inhibition was expected to result in longer retrograde run lengths and fewer pauses during retrograde movement, the opposite was observed. (While the observed results in the schematic approximate the published data, the expected results are only qualitative estimates for comparison). Figures adapted from Encalada et al.. B: Dynein in mouse neurons was inhibited by knocking down the p150 subunit of the dynein adaptor protein dynactin that links dynein to vesicles. Vesicles were labeled by RFP-LAMP1 and their transport analyzed by kymographs. In the control kymograph at top (scrambled RNAi), numerous bidirectional transport events (diagonal lines) were observed, while following dynein inhibition (p150 RNAi) very little transport in either direction was observed and almost all vesicles were stationary (vertical lines). Schematic plots of the data show that while dynein inhibition is expected to result in more anterograde transport events and fewer non-motile events, the opposite was observed. Figure adapted from Moughamian and Holzbaur.

Figure 5. Three hypothetical mechanisms for resolving the paradox of codependence

In the microtubule tethering mechanism, motors are proposed to transition between a strong-binding state when the motor is walking and a weak-binding state in which the motor is inactive but remains tethered to the microtubule. Diminished cargo transport in mutants results from a lack of tethering. In the mechanical activation mechanism, motors are posited to be in an inactive state until an opposing force pulls on and activates them. The absence of one class of motors in a mutant diminishes cargo transport because the opposing motor is not mechanically activated. In the steric disinhibition mechanism, motors are proposed to remain in an inhibited state even when bound to their cargo. Direct binding by opposing motors or other regulatory proteins relieves inhibition, resulting in transport. The diminished cargo transport in mutants results from motors remaining in their inhibited state.

Figure 6. Mathematical modeling framework for bidirectional transport models

Hypothetical mechanisms can be explored and quantitatively tested using mathematical models of cargo transport by kinesin and dynein motors. On a given cargo, there are N kinesin motors (Kin₁ to Kin_N) and M dynein motors (Dyn₁ to Dyn_M). Motor states are defined generically as On and Off, with different hypothetical mechanisms having different definitions of the on- and off-states (given at the right in order: microtubule tethering, mechanical activation, and steric disinhibition). For each motor, the rate constant k⁺ defines the rate that the motor switches from the off-state to the on-state, and the rate constant k defines the rate that the motor switches from the on-state to the off-state. These switching rates depend on different variables in the system (such as the force acting on that motor, the cargo position, or the activity of the opposing motors), and the switching rate magnitudes and parameter dependencies are the principal determinants of overall model behavior. The different hypothetical mechanisms presented in Figure 5 will have different dependencies for their switching rates; for example, the k⁺ dependencies for the three hypothetical mechanisms are given at the right. These models readily lend themselves to standard Monte Carlo (Gillespie Stochastic Simulation Algorithm) approaches.