Processive movement of single kinesins on crowded microtubules visualized using quantum dots

Abstract

Kinesin-1 is a processive molecular motor transporting cargo along microtubules. Inside cells, several motors and microtubule-associated proteins compete for binding to microtubules. Therefore, the question arises how processive movement of kinesin-1 is affected by crowding on the microtubule. Here we use total internal reflection fluorescence microscopy to image in vitro the runs of single quantum dot-labelled kinesins on crowded microtubules under steady-state conditions and to measure the degree of crowding on a microtubule at steady-state. We find that the runs of kinesins are little affected by high kinesin densities on a microtubule. However, the presence of high densities of a mutant kinesin that is not able to step efficiently reduces the average speed of wild-type kinesin, while hardly changing its processivity. This indicates that kinesin waits in a strongly bound state on the microtubule when encountering an obstacle until the obstacle unbinds and frees the binding site for kinesin's next step. A simple kinetic model can explain quantitatively the behaviour of kinesin under both crowding conditions.

Figure 1

Motility of kinesins coupled to quantum dots. (A) TIRF microscopy of quantum dot-labelled kinesins (green) moving along Texas Red-labelled microtubules (red). Concentrations were 5 nM biotinylated kinesin, 5 nM streptavidin-coated quantum dots, and 2 mM MgATP. Images of one microtubule at different time points are shown. The bar is 2 μm. (B) Kymographs of quantum dot-labelled kinesins moving along six different microtubules. Diagonal lines with constant slope represent runs with constant speed, while vertical lines represent immobile or pausing quantum dots. (C) Probability distribution of the individual run lengths of quantum dot-labelled kinesins with a linear regression yielding the average run length (left), and velocity histogram with a Gaussian fit yielding the average velocity (right) (see Materials and methods). (D) Average run lengths of kinesin–quantum dot conjugates at different kinesin–quantum dot mixing stoichiometries. Average run lengths were obtained either from a fit to the probability distribution of the individual run lengths (black squares, with black bars for the error of the fit) or as the mean of the run lengths (red circles with red bars for the standard deviation). The lines represent exponential fits to the data.

Figure 2

Binding of kinesin to microtubules. (A) TIRF microscopy of 150 nM kinesin–GFP (green) binding to surface-immobilized Texas Red-labelled microtubules (red) in the presence of 2 mM MgAMP–PNP (top) and in the presence of 2 mM MgATP (bottom). The bar is 10 μm. (B) Fluorescence intensities of kinesin–GFP bound to microtubules in the presence of 2 mM MgATP (red circles) and 2 mM MgAMP–PNP (black squares) at varying concentrations of kinesin–GFP, as measured by TIRF microscopy. The concentration of microtubules on the surface was estimated to be less than 1 nM. Red curves are hyperbolic fits assuming thermodynamic equilibrium binding (see Materials and methods). (C) Percentage of unbound kinesin after cosedimentation of 1 μM wild-type kinesin (left) or 1 μM E164A mutant (right) with 2 μM microtubules in the presence of 2 mM MgATP (green) or 2 mM MgAMP–PNP (red). Both wild-type and mutant kinesin were released to a large extent from the microtubules in the presence of MgATP, while they bind strongly in the presence of MgAMP–PNP.

Figure 3

Effect of kinesin–GFP on the motility of quantum dot-labelled single kinesins and of kinesin clusters. Motility parameters of the runs of 1 nM streptavidin-coated quantum dots associated with 1 nM biotinylated kinesin (left column: single motors) and 5 nM biotinylated kinesin (right column: multiple motors) in the presence of 0–0.9 μM kinesin–GFP and 2 mM MgATP. (A) Frequency of binding of quantum dot-labelled kinesins to microtubules (black squares) and the calculated error (black bars). (B) Average velocity of quantum dot-labelled kinesins calculated from a Gaussian fit to the velocity distribution (black squares) with standard deviation (black bars), and mean velocities (red circles) with standard deviation (red bars). (C) Average run length as obtained from a fit to the probability distribution of the individual run lengths (black squares), and mean travel distance (red circles) with standard deviation (red bars). (D) Average dwell time as obtained from a fit to the probability distribution of the individual dwell times (black squares) and the error of the fit parameter (black bars, not visible in most cases). The red lines are fits to the kinetic parameters at different kinesin–GFP concentrations based on the kinetic model illustrated in Figure 5 and as described in Materials and methods.

Figure 4

Effect of the mutant E164A on the motility of single quantum dot-labelled wild-type kinesins. Motility parameters of the runs of 1 nM quantum dots associated with 1 nM biotinylated wild-type kinesins in the presence of 0–1.6 μM mutant E164A and 2 mM MgATP. (A) Binding frequency of single quantum dot-labelled wild-type kinesins to microtubules (black squares) and the calculated error (black bars). (B) Average velocity of single quantum dot-labelled wild-type kinesins, as calculated from a Gaussian fit of the velocity distribution (black squares) plotted together with the standard deviation of the fitted distribution (black bars), and the mean velocities (red circles) plotted together with their standard deviation (red bars). (C) Average run length as obtained from a fit to the probability distribution of the individual run lengths (black squares), and the mean run length (red circles) with the standard deviation (red bars). (D) Average dwell time as obtained from a fit to the probability distribution of the individual dwell times (black squares) and the error of the fit parameter (black bars). The red lines are fits to the kinetic parameters at different mutant E164A concentrations based on the kinetic model illustrated in Figure 5 and as described in Materials and methods. Note the different scale of the abscissa as compared to Figure 3.

Figure 5

Simplified model for kinesin's biochemical cycle. Biochemical cycle with a strongly bound state S and a weakly bound state W of wild-type kinesin (orange) in the absence (A) and the presence (B) of an obstacle (red). The obstacle blocks kinesin in a strongly bound state B. The slow reverse reactions are neglected. The intermediate in brackets indicates that a real cycle consists of several more than two intermediates, which are omitted here for simplicity.