Correspondence to: Eric F. Wieschaus, Howard Hughes Medical Institute, Department of Molecular Biology, Princeton University, Princeton, NJ 08544. Tel:(609) 258-5383 Fax:(609) 258-1547 E-mail:ewieschaus{at}molbio.princeton.edu.
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Abstract |
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Cytoplasmic dynein is a microtubule-based motor with diverse cellular roles. Here, we use mutations in the dynein heavy chain gene to impair the motor's function, and employ biophysical measurements to demonstrate that cytoplasmic dynein is responsible for the minus end motion of bidirectionally moving lipid droplets in early Drosophila embryos. This analysis yields an estimate for the force that a single cytoplasmic dynein exerts in vivo (1.1 pN). It also allows us to quantitate dynein-mediated cargo motion in vivo, providing a framework for investigating how dynein's activity is controlled. We identify three distinct travel states whose general features also characterize plus end motion. These states are preserved in different developmental stages. We had previously provided evidence that for each travel direction, single droplets are moved by multiple motors of the same type (
Key Words: cytoplasmic dynein, processivity, vesicle, bidirectional, regulation
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Introduction |
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Cytoplasmic dynein is a minus enddirected microtubule-based motor that participates in a wide range of cellular transport processes, from mitosis, and retrograde axonal transport, to the accumulation of pigment granules in melanophores (
Because cytoplasmic dynein has to perform multiple and diverse tasks, it seems likely that its activity is highly controlled. Consequently, there is great interest in identifying regulators of dynein, and traditional approaches from genetics, biochemistry, and molecular biology have yielded promising candidates (for reviews see
Crucial insight into the mechanism that controls motor activity can be gained from the statistics of the distance traveled in a single run (i.e., a segment of uninterrupted motion): different processes ending runs are expected to lead to different types of distributions. For example, if motion ends (because at each step the motor falls off with a constant probability), then a histogram of travel distances would be described by a simple decaying exponential curve, which is the distribution observed in vitro for a bead moved by a single kinesin or axonemal or cytoplasmic dynein (
Such quantitative characterization is complicated because in many situations dynein-driven travel constantly alternates with excursions in the opposite direction (
We have previously described a new in vivo model system to study bidirectional transport: lipid droplets in early Drosophila embryos (
In this paper, we show that mutations in the dynein heavy chain gene alter the minus end motion of individual lipid droplets. Because dynein localizes to the lipid droplets, we conclude that dynein is responsible for the minus end transport of the droplets. Droplet motion is composed of pauses and runs of varying lengths, but droplets rarely diffuse out of the plane of focus. Using newly developed tools to automatically track and characterize droplet motion, we quantify dynein-driven cargo transport and find that under several different conditions both pauses and runs are characterized by simple exponential distributions. These observations exclude a large class of models describing how dynein activity is controlled. Most runs do not end in pauses, but result in immediate switching of travel direction. Because runs are shorter than expected if droplets are moved simultaneously by multiple motors, we propose that they are not limited by the processivity of the motors, but are instead cut short by a different mechanism.
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Materials and Methods |
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Immunolocalization
Embryos were disrupted on polylysine-treated glass slides (10-well slides with Teflon coating; Polysciences, Inc.) in 3 µl of buffer (35 mM Pipes, 5 mM MgSO4, 5 mM EGTA, 0.5 mM EDTA, plus a cocktail of protease inhibitors;
To judge how large the signal from a single dynein molecule would appear in these preparations, we estimated that the bridge provided by primary, secondary, and tertiary antibodies would position any one fluorophore up to 50 nm from the antigen (fully extended antibody molecules are ~25 nm long;
Force Measurements
Force measurements were performed as described in
Particle Tracking and Analysis
As described in
The motion of individual droplets was determined by tracking them using the Isee image-processing software package (Inovision Corp.). Two types of calibrations of the accuracy of position detection were performed. First, the inherent noise of this process was determined to be ~±8 nm p-p, found by recording and tracking images of a droplet fixed to the coverslip. Second, the subpixel resolution of the tracking software was confirmed by tracking images of a bead affixed to a translational stage moved with nanometer resolution in a triangle wave. No deviations from linear displacement were observed, and the residual (i.e., the difference between the data points and a straight line fit to the data) was zero mean and again showed roughly 8-nm p-p noise.
Once the x-y location of individual droplets was known as a function of time, motion along a particular microtubule was determined by projecting the motion onto the best fit line to the droplet's trajectory (
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To avoid the effects of noise, for a displacement to be scored as a run it needed to be at least 30-nm long, and last at least 0.16 s. Pauses were operationally defined as periods when there was <30 nm displacement for a duration of at least 0.23 s, with the additional condition that the mean velocity had to be <50 nm/s.
Because a combination of algorithms was used to determine when a droplet was moving or paused, the parsing program was tested in two ways. First, the program was tested on a data set previously analyzed by hand, and the two methods yielded the same results. Next, the program was run on simulated data with noise (8 nm, peak-to-peak, experimentally derived from tracking a fixed droplet), where the plus and minus end runs were drawn from double exponential distributions with known distance constants, and the pauses were drawn from a single exponential distribution with known time constant. The distance and time constants were chosen to be approximately the same as those found experimentally. The simulated data were treated as experimental data and analyzed in the same manner. This produced histograms similar to the histogram in Fig 6, and the correct distance constants were recovered (to within experimental error) when the histograms were fit to a double exponential. The chi-squared values of the fits to the simulated data were in the same range as the fits to the experimental data.
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To check for the possibility of artifacts in data analysis, the analysis was also run on simulated data with noise (8 nm, peak-to-peak, experimentally derived from tracking a fixed droplet), where the plus and minus end runs were drawn from single exponential distributions with known distance constants, and the pauses were drawn from a single exponential distribution with known time constant. In this case, the resultant histograms were well described by a single decaying exponential as judged by chi-squared values, and the correct distance constants were recovered to within experimental error.
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Results |
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Cytoplasmic Dynein Transports Lipid Droplets
Based on the relatively high speed of droplet transport, we had previously proposed that minus end motion employed cytoplasmic dynein (
To test whether this localization was functionally important, we investigated whether lipid droplet motion was altered when dynein function was impaired genetically. Ideally, one would inspect droplet behavior in embryos in which dynein function is completely eliminated. However, this was not possible because cytoplasmic dynein is provided to the early embryo by the mother and is necessary for the earlier processes of oogenesis (
However, global transport of lipid droplets was disrupted in the Dhc64C mutant embryos (Fig 2). In the wild type, there are three phases of net droplet transport (
Characterization of the motion of individual lipid droplets demonstrated that in the Dhc64C mutants, transport was already altered during phase II. Using optical tweezers and the previously described squash-mount preparations of embryos (
We have previously argued that lipid droplets carry multiple motors of the same kind, each capable of exerting a stalling force of 1.1 pN (
Quantifying Dynein-driven Droplet Motion
The identification of cytoplasmic dynein as the relevant minus end motor allowed us to use this experimental system to examine the properties of wild-type dynein-driven cargo transport in vivo. Since it is the balance between minus and plus end motion that determines the direction of net transport (
Pause Durations Are Characterized by a Decaying Exponential Distribution
To our knowledge, there has been no previous quantitation of pausing for any microtubule-based transport, either in vitro or in vivo. Thus, it is unclear whether pauses play an important role in determining the properties of transport. Pauses might constitute irrelevant interruptions, unrelated to the processes that control travel distance, or they might be crucial points of transition. For example, when RNA polymerase advances along its DNA substrate during transcription, pauses are associated with the action of regulatory mechanisms and can result in transcription termination (
For lipid droplets, we defined pauses operationally as periods when the droplet remained motionless (average velocity of <50 nm/s) for at least 0.23 s (corresponding to seven successive video frames). Such pauses appeared to constitute a well-defined state: there was no net displacement, with roughly Gaussian zero-mean histograms of this random displacement (data not shown). Additionally, paused droplets showed no significant velocity: they moved at average speeds of <14 nm/s, well separated from the typical speed of 150 nm/s of even the slowest moving droplets (see below).
Because pauses after minus end motion ("minus-pauses") are potentially different from pauses after plus end motion ("plus-pauses"), minus- and plus-pauses were analyzed separately. To characterize minus end travel, we focus here on minus-pauses. On average, minus-pauses occurred approximately every 5 s of uninterrupted travel and lasted for an average of ~0.6 s. Droplets spent ~9% of the time in pauses (Table 1). Pause duration was well described by a single decaying exponential y(t) = A exp(-t/t0) (Fig 5 A). This functional form suggests that the probability of exiting from a pause is constant over time. For a decaying exponential, the time constant t0 is equal to the average duration of the pause.
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We next asked if there was a link between pauses and the other discontinuity in droplet motion, reversal of direction. To quantitate, we calculated the percentage of pauses resulting in reversals (i.e., a minus-pause followed by plus end motion); and the fraction of total reversals associated with a pause. Reversals and pauses can occur in the absence of one another, and most pauses do not lead to reversals. While pauses might in principle be important in altering net transport by cutting plus or minus end travel short and inducing reversals, in fact their impact seems to be limited. Only ~13% of the reversals are associated with pauses (Table 1), and the percentage of plus-to-minus reversals associated with the pauses (11.7%) is roughly the same as the percentage of minus-to-plus reversals associated with pauses (Table 1, 13.8%).
Since our detection threshold for pauses is 0.23 s, there may be additional reversals that are associated with very short periods of cessation in motion that are below our detection threshold. To estimate the frequency of such reversals, we fit an exponential to the 100 pauses that do result in a minus-plus reversal (A = 46 ± 9, t0= 0.4 ± 0.04, 2 = 0.59). By extrapolation, we would predict an additional 110 reversals that were associated with pauses too short to be detected. However, there were roughly 800 minus-plus reversals observed in the data set. This leaves the majority of reversals (~590) unassociated with pauses, suggesting that reversals occur primarily independent of pauses, and that pauses are not crucial for understanding how far droplets move towards the minus end before reversing course.
Distribution of Run Lengths
Since reversals occur independent of pauses, we investigated whether they could be related to the lengths of runs by examining the periods when droplets are moving uninterrupted in the minus end direction. Such runs varied greatly in length, from 30 (at the limit of resolution for our method to unambiguously determine a moving droplet) to >4,000 nm, with mean velocities during the entire run from 150 to 2,000 nm/s. Because pauses were relatively rare, most of these runs were directly followed by a run in the plus end direction. The distribution of run lengths provides insight into how travel distance is controlled: a process that acts with constant probability (such as motors falling off) would result in a distribution described by a simple decaying exponential, whereas mechanisms that turn off the motor after a certain distance has been traveled would result in clustering of travel distances around that value. Further scenarios are possible.
The histogram of phase II run lengths (Fig 6 A) approximated a simple, monotonically decaying curve, reminiscent of the decaying exponential distributions observed for various individual motors in vitro (2 values were at best 3, corresponding to a probability of <0.01% that the observed distribution was described by a single decaying exponential. This failure to find a good fit might simply be an artifact, if there were systematic errors in the underlying data. However, the observed random noise of the position versus time data was small (8 nm, peak-to-peak, see Materials and Methods), so that the error associated with a particular bin is relatively independent of the mean values of the runs in that bin. Indeed, when we use the analysis algorithm on simulated position versus time data with added noise (with individual plus and minus end runs drawn from single decaying exponential distributions), our analysis yielded histograms that were well fit by this functional form, with the correct distance constants (see Materials and Methods). Thus, a single decaying exponential could have been detected if it were present, but does not account for the distribution of travel lengths we observe in vivo.
This analysis suggested that the underlying distribution was more complicated. The histogram was indeed described (2 = 1.20; P = 19%) by the sum of two decaying exponentials y(D) = AS exp(-D/DS) + AL exp(-D/DL), with distance constants DS = 98 nm and DL = 1,068 nm (Fig 6 A, Table 2). Using simulated data with added experimentally derived noise, we confirmed that our analysis did in fact recover the correct distance constants and amplitudes for data described by the sum of two decaying exponentials (see Materials and Methods). Additionally, trying to fit this simulated data with a single decaying exponential did not yield a good chi-squared value.
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One possible explanation for the need to use the sum of two decaying exponentials to describe the histograms is that there are two distinct states of droplet travel: short runs (average travel distance 98 nm) and long runs (average travel distance 1,068 nm). Consistent with this view, droplets traveling for short distances have lower speeds than those traveling long distances (Fig 7). To determine the relative frequency of these states, we derived the ratio RSL of the number of short runs and long runs from the exponential fits (see Appendix). There were roughly twice as many short runs as long runs (RSL= 2.15, Table 2).
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Run Lengths Are Described by the Same Functional Form under Different Conditions
It is possible that the biologically relevant description of the distribution of travel distances is another functional form (function A), and that the sum of two decaying exponentials simply happened to be a reasonable approximation of function A in the particular case we examined. Under different conditions, function A might vary and would no longer be well approximated by the sum of two exponentials. To address this issue, we examined motion in the Dhc64C mutant and at different developmental stages.
In the Dhc64C mutant, our analysis of stall forces (Fig 3) had revealed that dynein-mediated droplet transport is already impaired during phase II. However, qualitatively, the motion of droplets was nevertheless very similar to the motion in the wild type, with runs of varying lengths, reversals, and pauses. Pauses were again not obligatorily linked to reversals in direction (Table 1), but they were longer and their frequency was increased (Table 1). Runs in the minus end direction were greatly affected, with average distance traveled reduced by more than a factor of three (Table 2). Despite this, the shape of the distribution of run lengths resembled the one observed in the wild type (Fig 6 B). It was again not possible to fit this histogram to a single decaying exponential (not shown), but the sum of two exponentials provided an excellent fit (Table 2, 2 = 0.41, P = 97%). The relative frequency of short versus long runs was indistinguishable from the wild type (RSL, Table 2). However, both distance constants were markedly reduced, suggesting that both short and long runs employ cytoplasmic dynein.
As a second test of how generally the two exponential functional form describes the distribution of droplet travel, we analyzed droplet motion in wild-type embryos during phase III. Relative to phase II, the direction of net transport in phase III is reversed and towards the minus end, and stall forces are reduced (2 = 0.93, P = 68%). Interestingly, this distribution was also quantitatively the same as the phase II distribution (Fig 6 A): the magnitude of both distance constants and the relative frequency of short to long runs were indistinguishable in the two phases (Table 2).
Thus, in all three cases examined, the distribution of travel distances is well described by the sum of two exponentials. This suggests that this functional form reflects general attributes of dynein behavior in vivo. We propose the existence of two distinct states of dynein-driven droplet travel, responsible for short and long runs, respectively. Because in all cases short runs had lower velocities than the corresponding long runs (Fig 7), we will refer in the following to short-slow and long-fast travel states.
Control of Net Transport
Previously, we had shown that the net transport of lipid droplets results from the balance between plus and minus end transport, and that the developmental shift in net transport is due to a change in the average length of plus end motion, whereas the average length of minus end motion remains constant (
Qualitatively, plus end motion was similar to minus end motion. Pauses and reversals could also occur independently of each other, and pause lengths fit a single decaying exponential (Fig 5 B). Unlike minus end travel, the average distance traveled between phase II and phase III varied by roughly a factor of two (Table 2). Nevertheless, the distribution of run lengths in both phases was well described by the sum of two exponentials (Fig 8a and Fig b, Table 2), and short and long runs differed in their velocities (data not shown). Thus, motion driven by the as-yet-unidentified motors powering motion in the plus end direction can also be broken down into pauses: short-slow travel and long-fast travel.
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Plus end motion was qualitatively similar in both phase II and phase III, suggesting that the same processes regulate motion in the two phases. This framework enabled us to ask which quantitative changes are responsible for the decrease in average distance traveled between phases II and III (Table 2). There were two: droplets in the long-fast state traveled for significantly shorter distances (Table 2, DL), and fewer droplets were in this state (Table 2, RSL). Surprisingly, the distance constant for the short-slow travel state (DSL) even increased somewhat (Table 2), which would tend to increase overall travel distance, but this was not enough to compensate for the other changes. Thus, the developmentally controlled reversal in net transport is brought about by changes in two parameters of plus end motion: the relative frequency of the two travel states and the distance constant for long-fast travel.
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Discussion |
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Many cytoplasmic organelles show bidirectional movement along microtubules (
In vitro, motors have a finite probability of falling off the microtubule at each attempted step. If the distances cargoes travel in vivo reflect this processivity, characterization of the motor itself might be sufficient to understand how travel lengths are determined in a cell. However, if motor processivity is not limiting, other mechanisms must terminate runs, and their properties have to be unraveled to understand what controls travel distance. For lipid droplet motion, we propose the following model: run lengths are determined not by inherent motor processivity but by an additional mechanism. This mechanism acts as a switch, both ending runs and immediately reversing travel direction.
Long-Fast Travel: The Relationship between Motor Processivity and Run Length
To evaluate our model, we can take advantage of the stall force measurements that allow us to estimate the number of active motors on the droplets. The stall forces vary developmentally in a quantized fashion, suggesting that lipid droplets simultaneously engage multiple motors of the same type (
If droplet travel distances are solely determined by the processivity of multiple independently acting motors, travel distance should increase with motor number because when one motor detaches from the microtubule, the others remain bound, giving the detached motor a chance to reengage. This increased travel distance is indeed observed in vitro, where beads with multiple kinesin motors move distances much longer (>4 µm, ended by encountering the end of the microtubule) than the 1,400-nm average distance traveled by beads with single motors on them (
A clear prediction of this model is that travel distances should be shorter than expected from the inherent processivity of the motors. In vitro, a single cytoplasmic dynein can move glass beads processively for an average distance of 800 nm (
This conclusion is also supported by the rarity of pauses. If runs ended because of limited motor processivity, they should typically be followed by pauses because the motors would no longer be attached to the microtubule. However, in vivo, runs are usually followed by immediate reversals (see also below).
Properties of the Mechanism that Terminates Long-Fast Runs
What type of process might end runs before the inherent motor processivity becomes limiting? A study of the in vitro behavior of motors (
Thus, a novel mechanism must be responsible for determining travel distance on lipid droplets. Our analysis allows us to characterize some of its properties. The fact that the long-fast travel distances are described by a single exponential distribution suggests that the probability of ending a run remains constant during travel. This observation is not consistent with a mechanism that measures travel distances, e.g., one for which the likelihood of a run ending increases when the droplet has traveled a certain distance. Rather, it suggests that the mechanism that terminates a run is governed by a single rate-limiting step.
When runs end, are motors simply detached from their track? In that case, runs should typically be followed by the droplet diffusing away or pausing. However, such diffusion is almost never seen and pauses are rare. Instead, when droplets reverse direction of travel, they move in the opposite direction without delay, as if the motor for the opposite direction of travel becomes active as soon as the activity of the other motor ends. This observation suggests that the activity of the opposing motors is closely linked. Therefore, we propose that the process responsible for ending runs is a switch, which both coordinates plus and minus end motors, and determines when runs end.
The motor for plus end travel has not yet been identified. Thus, we cannot determine if long-fast plus end runs are also shorter than predicted from motor processivity in vitro. However, because this travel state is described by a single exponential distribution and is followed by immediate reversals, it seems likely that it is also governed by a switch. Since the probability for long-fast plus end travel to terminate changes during development, and its change controls the direction of net droplet transport, the cell can apparently regulate the properties of these switches.
The Short-Slow Travel State
Short-slow travel shares many qualitative properties with long-fast travel. Run lengths are characterized by a single exponential distribution, and the end of runs are followed by immediate reversals in travel direction. However, quantitatively short-slow travel displays much shorter distance constants and reduced travel velocities. These differences might suggest that droplets in these two states are powered by distinct motors. However, for minus end travel, both states are altered in the Dhc64C mutant (Table 2), implying that they are due to cytoplasmic dynein functioning in two different modes.
One possibility is that the short-slow state is due to interference between the multiple motors on the lipid droplet. For example, a single dynein might occasionally lock-up, possibly because communication between its two head domains fails (
Our quantitation of dynein-driven cargo movement in early Drosophila embryos provides a conceptual framework for investigating the molecular basis of transport regulation in vivo. We are proposing the existence of switches that end runs and coordinate opposite polarity motors. The challenge for the future will be to identify the molecular components of these switches and to determine which of their biochemical properties control the probability for terminating a run. Given dynein's diverse roles in many cellular transport processes, similar switches will likely be crucial for determining net transport of other organelles wherever bidirectional transport is observed.
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Footnotes |
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The current address for S.P. Gross is Department of Developmental Cell Biology, University of California, Irvine, Irvine, CA 92697-2300.
The current address for S.M. Block is Department of Applied Physics, Stanford University, Stanford, CA 94305.
1 Abbreviation used in this paper: Dic, dynein intermediate chain.
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Acknowledgements |
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We thank Tom Hays (Department of Genetics, University of Minnesota, Minneapolis, MN) for providing fly stocks, and Susan Gilbert, Amanda Norvell, and Trudi Schüpbach (all from Department of Molecular Biology, Princeton University, Princeton, NJ) for helpful comments on the manuscript. M.A. Welte thanks Joe Goodhouse (Department of Molecular Biology, Princeton University, Princeton, NJ) for expert technical assistance. S.P. Gross thanks William Saxton (Institute for Molecular/Cellular Biology, University of Indiana, Bloomington, Bloomington, IN) and Susan Gilbert for helpful discussions.
S.P. Gross is a recipient of the National Institutes of Health (NIH) postdoctoral traineeship 5F32GM18329 from the NIGMS. S.M. Block acknowledges support by grants from the National Science Foundation, NIH, and the W.M. Keck Foundation. S.P. Gross, E.F. Wieschaus, and M.A. Welte gratefully acknowledge support from the Howard Hughes Medical Institute and grant 5R37HD15587 from the National Institute of Child Health and Human Development.
Submitted: 15 October 1999
Revised: 2 February 2000
Accepted: 3 February 2000
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RSL was calculated according to the formula
The long amplitude, AL, was set to one by dividing it by the original long amplitude, ALRaw, and the associated error was calculated by dividing the raw standard error (from the fit) by ALRaw. The corresponding short amplitude AS was calculated by dividing the raw short amplitude ASRaw by ALRaw, and its associated error was also divided by ALRaw.
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References |
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