Transportation of nanoscale cargoes by myosin propelled actin filaments

Abstract

Myosin II propelled actin filaments move ten times faster than kinesin driven microtubules and are thus attractive candidates as cargo-transporting shuttles in motor driven lab-on-a-chip devices. In addition, actomyosin-based transportation of nanoparticles is useful in various fundamental studies. However, it is poorly understood how actomyosin function is affected by different number of nanoscale cargoes, by cargo size, and by the mode of cargo-attachment to the actin filament. This is studied here using biotin/fluorophores, streptavidin, streptavidin-coated quantum dots, and liposomes as model cargoes attached to monomers along the actin filaments ("side-attached") or to the trailing filament end via the plus end capping protein CapZ. Long-distance transportation (>100 µm) could be seen for all cargoes independently of attachment mode but the fraction of motile filaments decreased with increasing number of side-attached cargoes, a reduction that occurred within a range of 10-50 streptavidin molecules, 1-10 quantum dots or with just 1 liposome. However, as observed by monitoring these motile filaments with the attached cargo, the velocity was little affected. This also applied for end-attached cargoes where the attachment was mediated by CapZ. The results with side-attached cargoes argue against certain models for chemomechanical energy transduction in actomyosin and give important insights of relevance for effective exploitation of actomyosin-based cargo-transportation in molecular diagnostics and other nanotechnological applications. The attachment of quantum dots via CapZ, without appreciable modulation of actomyosin function, is useful in fundamental studies as exemplified here by tracking with nanometer accuracy.

Figure 1. Effect of different degrees of streptavidin labelling on HMM induced actin filament motility.

A. Biotinylated and APh labelled F-actin (5 nM), immobilized to HMM on a surface, then incubated with 1 nM TRITC-streptavidin for 1 min followed by rinsing and incubation by AMc130 assay solution. Observation using TRITC filter set. Arrow indicates motile TRITC-streptavidin labelled filament. Other filament-like objects represent cross-linked and non-motile filaments. Inset: nanospool of actin filament formed by intra-filament biotin-streptavidin cross-linking. Left image, TRITC-filter set to observe TRITC-streptavidin. Right image, FITC-filter set to observe Alexa-488-labelling of the same filament. B. Same conditions as in A except for incubation with 20 nM TRITC-streptavidin and 40 µM biotin in solution. Arrows indicate motile filaments. Note, brighter background than in A, presumably due to non-specific binding of TRITC-streptavidin. C. Fraction of motile filaments vs. average number of streptavidin molecules per µm filament length. Vertical error bars: SEM. Horizontal error bars: standard deviation. D. Velocity for filaments with different number of streptavidin molecules measured during the periods of smoothest sliding (CV <0.5). E. Average velocity during 6–16 s plotted against number of streptavidin molecules per filament length. Black and grey symbols: two different experimental dates with different HMM, actin and streptavidin batches. Red symbols: covalently biotinylated F-actin with addition of 0–20 nM TRITC-streptavidin followed by 20 nM to 40 µM biotin, prior to motility assay. Same experimental date as black. Straight full lines obtained by linear regression as well as dashed lines representing 95% confidence intervals.

Figure 2. Motility quality for biotinylated actin filaments with 1–2 quantum dot cargoes attached along the filaments.

A. Fraction of motile filaments in the absence (black) and presence (grey) of quantum dots. Error bars represent SEM. B. Sliding velocity for biotinylated F-actin without (black) and with (grey) quantum dots. Error bars represent 95% confidence intervals. Number of filaments given in parentheses.

Figure 3. Motility quality vs. number of attached quantum dots on HMM propelled filaments.

A. Fluorescence micrograph time series at 3 s intervals showing several motile and some non-motile and heavily quantum dot labelled actin filaments. All fluorescence in the micrographs is attributed to the quantum dots. The colour coded arrows point to some motile filaments/aggregates with 5 quantum dots (blue arrow; apparently single filament), >5 quantum dots and presumably 2 cross-linked filaments (orange arrow), small aggregate (white arrow), large aggregate (red arrow). B. The average fraction of motile filaments in 3 different experiments. Error bars: SEM (n = 3 different flow cells and experimental occasions for different number of quantum dots; >3–11 quantum dot-labelled filaments per flow cell). The solid line (included for descriptive purpose) represents a single exponential function decaying with increased number of quantum dots to a plateau value. Optimal parameter values and 95% confidence interval (dotted lines) obtained by non-linear regression analysis. C. The mean sliding velocity ±95% confidence intervals in 3 (or 4; for 1–3 quantum dots) different experiments. The filled symbols and solid line represent the ten frames running average of the frame-to-frame velocities with lowest CV on the condition that CV<0.5. The open symbols and dashed line, on the other hand, represents average velocities calculated by dividing the integrated sliding distance with the tracking duration (11–14 s; shorter in the few cases when filaments moved out of the image).

Figure 4. Motility quality vs. number of attached liposomes on HMM propelled filaments. A.

The fraction of motile filaments at two different experimental dates (black and grey). B. The sliding velocity. Velocity data represented by black and grey represent two different experiments. The filled symbol and solid line represent velocities obtained from the ten frames running average of the frame-to-frame velocities with lowest CV (CV<0.5), while the open symbols and dashed line represents the average velocity as described in Fig. 3. The numbers in parentheses represent the number of filaments studied for each condition (>10 in B unless otherwise stated). Error bars: SEM. Temperature: 21°C.

Figure 5. Effect of CapZ binding to F-actin on HMM propelled actin filament velocity. A

. Sliding velocities at different ionic strengths for F-actin with (grey) and without (black) CapZ/quantum dot complex (n = 3 experimental occasions and different HMM preparations unless indicated in figure; >17 filaments analysed for each condition). Temperature: 22°C. Inset: two subsequent images (interval: 1.6 s) of HMM propelled RhPh-labelled F-actin (sliding to the right) with quantum dot attached to the trailing end via CapZ. B. Sliding velocities for F-actin capped with CapZ-rhodamine (dashed line, open symbols), CapZ/quantum dot complex (grey) and without CapZ (black) in one given experiment (number of filaments in parentheses). C. Effects of CapZ on sliding velocity at high temperature (28.6°C) in AMc130 assay solution. Average sliding velocities for F-actin in the absence of CapZ or in the precence of CapZ/quantum dot complex. Filament lengths of F-actin without CapZ limited to the mean length of CapZ capped actin filaments ±2 standard deviations (0–3 µm; see further Fig. S6, Fig. S7) to ensure comparability. Error bars in A–C: 95% CI. D. Velocity measurements (5 frames s⁻¹) at low [MgATP] using nanometer tracking of quantum dots attached to CapZ (grey) or tracking the filament centroid (black) for the same filament (number of filaments in parentheses). Note appreciable overestimation of velocity at low [MgATP] by centroid tracking due to noise as most clearly indicated by the tracking of stationary filaments. The tracking of stationary quantum dots using the Gaussian fit (nanometer tracking) suggests a precision of <5 nm in each position estimate. This is based on the apparent speed of stationary quantum dots of 78 nm/s, a frame rate of 5 s⁻¹, the fact that the speed is a scalar quantity in the analysis and that each distance between frames, used for the velocity calculations, depends on two position estimates by Gaussian fits.