EB1 accelerates two conformational transitions important for microtubule maturation and dynamics - PubMed (original) (raw)

EB1 accelerates two conformational transitions important for microtubule maturation and dynamics

Sebastian P Maurer et al. Curr Biol. 2014.

Abstract

Background: The dynamic properties of microtubules depend on complex nanoscale structural rearrangements in their end regions. Members of the EB1 and XMAP215 protein families interact autonomously with microtubule ends. EB1 recruits several other proteins to growing microtubule ends and has seemingly antagonistic effects on microtubule dynamics: it induces catastrophes, and it increases growth velocity, as does the polymerase XMAP215.

Results: Using a combination of in vitro reconstitution, time-lapse fluorescence microscopy, and subpixel-precision image analysis and convolved model fitting, we have studied the effects of EB1 on conformational transitions in growing microtubule ends and on the time course of catastrophes. EB1 density distributions at growing microtubule ends reveal two consecutive conformational transitions in the microtubule end region, which have growth-velocity-independent kinetics. EB1 binds to the microtubule after the first and before the second conformational transition has occurred, positioning it several tens of nanometers behind XMAP215, which binds to the extreme microtubule end. EB1 binding accelerates conformational maturation in the microtubule, most likely by promoting lateral protofilament interactions and by accelerating reactions of the guanosine triphosphate (GTP) hydrolysis cycle. The microtubule maturation time is directly linked to the duration of a growth pause just before microtubule depolymerization, indicating an important role of the maturation time for the control of dynamic instability.

Conclusions: These activities establish EB1 as a microtubule maturation factor and provide a mechanistic explanation for its effects on microtubule growth and catastrophe frequency, which cause microtubules to be more dynamic.

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Figures

Figure 1

Automated Microtubule End Tracking and Averaging of Fluorescence Intensity Profiles with Subpixel Precision (A) Steps leading to time-averaged dual-color TIRF microscopy images of individual growing microtubule end regions. End positions of growing Cy5-microtubules are automatically determined at different times (t1–t3) with subpixel precision by 2D model fitting. The Cy5 and the GFP channels are locally registered around microtubule ends (Supplemental Experimental Procedures; Figure S1). Subsequently, 6.4 × 6.4 μm images, centered with subpixel precision at the microtubule end reference position, are cropped from the original images (white dashed boxes are guides only). The cropped images (typically 80–100) are then averaged to yield time-averaged images of individual microtubules and of their associated proteins, with an improved signal-to-noise ratio. Next, 2D models are fitted to the time-averaged images, and the microtubule axis and end position are determined with subpixel precision. One-dimensional intensity profiles along the microtubule axis (indicated by dotted white lines) are extracted from the time-averaged images in the two channels. Finally, several (typically 20–100) one-dimensional intensity profiles obtained from different microtubule growth episodes are superaveraged after alignment with respect to the microtubule end position. Typically, information from 1,000 to 10,000 individual image frames is included in the superaveraged profiles. Error bars are SE. (B) Visualization of the effect of convolving a δ function (blue), an exponentially decaying function (green), and a step function (red) with a Gaussian of width σ (black). This illustrates the difference between molecular density distributions and measured fluorescence intensity profiles. See also Figure S1.

Figure 2

Effect of Microtubule Growth Velocity on the EB1-GFP and XMAP215-GFP Fluorescence Intensity Profiles at Growing Microtubule Ends (A) Superaveraged images of Cy5-microtubule ends growing at different mean growth velocities vg in the presence of 1 nM EB1-GFP. Microtubules were selected for averaging according to their growth speed from experiments with 16.5–38 μM tubulin (see Figure S2B). (B and C) Superaveraged fluorescence intensity profiles of the Cy5-microtubule end (B) and EB1-GFP signal (C). Profiles contain information from 3,200 to 7,800 raw images in total. (D) The start positions of the assumed monoexponential EB1-GFP density distributions (black points), relative to the microtubule end, determined from (F). Also shown are the apparent peak positions obtained from a cursory inspection of the profiles in (C). (E and F) Calculated molecular density distributions derived from the fits to the Cy5-microtubule profiles (E) using an error function (black lines in B) and from the EB1-GFP profiles (F) using an exponentially modified Gauss model (black lines in C). (G–L) As in (A)–(F), for Cy5-microtubule ends (11.3 μM tubulin) growing at different mean velocities in the presence of 150 nM XMAP215-GFP. The superaveraged profiles were generated from 1,600 to 3,400 raw images in total. As for the experiments with EB1-GFP, no significant changes in the microtubule end profiles were detected with increasing growth velocity (H and J). Fits (black lines) to the XMAP215-GFP data in (I) use a Gauss-lattice model. Error bars are SE. See also Figure S2.

Figure 3

A One-Step Microtubule Maturation Model with Binding Kinetics Does Not Explain the EB1-GFP Intensity Profiles (A) Simple one-step kinetic model of microtubule maturation and EB1 binding and a schematic of a microtubule growing from right to left. Different colors illustrate the two different states (B and C) of the microtubule; green dots indicate bound EB1 molecules. Binding and unbinding kinetics of EB1 are characterized by the rate constants kon and koff, respectively. The B state transforms at a rate k2 into the mature microtubule lattice conformation, C. (B) Top: histogram of dwell times of single EB1-GFP molecules (10 pM) at growing Cy5-microtubule ends (20 μM tubulin). A mean dwell time of 290 ± 20 ms was obtained from a monoexponential fit. Bottom: histogram of waiting times between EB1-GFP binding events; a mean waiting time of 8.7 ± 0.6 s was obtained from a monoexponential fit. (C) Average maximum EB1-GFP fluorescence intensities at growing Cy5-microtubule ends (20 μM tubulin) as a function of EB1-GFP concentrations. For each data point, intensities from at least 1,451 images were averaged. A fit to the data (red line) using a one-site binding model yields a dissociation constant KD of 22 ± 1 nM. Inset: enlarged view of the lowest concentrations, showing no indication of cooperative binding. (D) The effect of EB1 binding kinetics on theoretical fluorescence intensity profiles. Green data points show the superaveraged EB1-GFP fluorescence intensity profile for an average growth speed of 95 nm/s. The solid lines show fits to the data using the one-step binding model without (red) and with (blue) inclusion of the EB1 binding kinetics (kon = koff/KD = 0.15 nM−1s−1 and koff = 3.4 s−1; the effective point spread function σ was fixed at 175 nm in both cases). Error bars are SE. See also Figure S3.

Figure 4

Two-Step Model of Microtubule Maturation (A) Kinetic model as in Figure 3A, but with an additional immature nonbinding state, A, that transforms at a rate k1 into an EB1 binding site, B. (B) A global fit (black lines) to the three superaveraged EB1-GFP profiles from Figure 2C (green data points) using the two-step maturation model with binding kinetics. Fitting parameters are summarized in Table S1. Inset: two examples of time-averaged EB1 signals at constant EB1 concentration and different microtubule growth rates. Error bars are SE. (C) Probability distributions of the EB1 binding site as calculated from the fits shown in (B). (D) Peak positions of the probability distributions at different growth velocities. Filled black circles and open circles relate to (C) and Figure 6A, respectively. (E) Theoretical number of tubulin dimers in maturation states A and B (bound and unbound to EB1), calculated from rate constants obtained from the fits in (B). See also Figure S4.

Figure 5

The Effect of EB1 on Microtubule Maturation (A) The kinetic model shown in Figure 4A, with addition of the rate k3 at which the EB-bound state BE transforms into C. (B) Superaveraged EB1-GFP fluorescence intensity profiles for microtubules growing with a mean velocity of 95 nm/s in the presence of varying EB1-GFP concentrations. The superaveraged profiles were generated from 1,881 to 2,022 raw images per condition. The black lines show a global fit to the three intensity profiles using the extended two-step maturation model with binding kinetics. An offset in the model accounts for weak lattice binding, which is noticeable at higher EB1 concentrations. Fitting parameters are summarized in Table S1. Inset: two examples of time-averaged EB1 signals at constant microtubule growth rates and different EB1 concentrations. Error bars are SE. (C and D) Probability distributions (C) of the EB1 binding site as calculated from the fits shown in (B), and corresponding peak positions (D). (E) Theoretical number of tubulin dimers in maturation states A and B (bound and unbound to EB1), calculated from rate constants obtained from the fits in (B). See also Figure S5.

Figure 6

Combined Effects of EB1 and XMAP215 (A) Superaveraged intensity profiles from 1 nM EB1-GFP in the absence (green data) and presence (orange data) of 150 nM XMAP215. The black lines are a global fit to both profiles with fixed kinetic rates (k1 = 1.39, k2 = 0.24, k3 = 0) and different growth velocities (64 and 107 nm/s, respectively). The peak positions of the corresponding molecular distributions are shown in Figure 4D. (B) Superaveraged intensity profiles from 20 nM XMAP215-GFP in the absence (blue data) and presence (orange data) of 150 nM EB1. Black lines are Gauss-lattice fits to the profiles. (C) TIRF microscopy images of EB1-GFP (green) and XMAP215-GFP (blue) on Cy5-labeled microtubules (red) grown in the presence of GTPγS. (D) Schematic illustrating the different maturation states and the effect of EB1 on their transitions. Error bars are SE. See also Figure S6.

Figure 7

The Effect of Microtubule Growth Velocity and EB1 Concentration on Pause Times and EB1 Intensity Decay Rates before Catastrophes (A) Averaged microtubule end position aligned at the catastrophe time point (see Supplemental Experimental Procedures) at different tubulin concentrations and hence growth velocities (50 nM EB1-GFP). The gray lines indicate growth pauses before catastrophe (Supplemental Experimental Procedures). (C) Normalized averaged EB1-GFP intensity-time profiles corresponding to (A). Solid lines are exponential fits to the data. The dashed and dotted horizontal lines give the average residual EB1-GFP intensity at catastrophe and error, respectively. (B and D) As in (A) and (C), for varying EB1-GFP concentrations (20 μM tubulin). Note that the pause time at 50 nM EB1-GFP in (B) is comparable to the pause times shown in (A) at the same EB1-GFP concentration and different tubulin concentrations. (E) Pause times (upper panel) and EB1-GFP decay times (lower panel) before catastrophe, extracted from (A)–(D), versus total maturation time (defined in the text). (F) Average time until catastrophe (see Figure S7A) versus total maturation time. (G) Schematic illustrating the change in microtubule end structure before a catastrophe. The orange-colored tubulins represent tubulins in the stabilizing B state; the green ovals represent bound EB1. Between 44 and 90 catastrophes were averaged for each condition. Error bars are SE. See also Figure S7.

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EB1 accelerates two conformational transitions important for microtubule maturation and dynamics - PubMed (original) (raw)