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. 2013 May;35(5):452-61.
doi: 10.1002/bies.201200131. Epub 2013 Mar 27.

Microtubule dynamic instability: a new model with coupled GTP hydrolysis and multistep catastrophe

Hugo Bowne-Anderson  1 , Marija Zanic, Monika Kauer, Jonathon Howard
Affiliations
Free PMC article

Microtubule dynamic instability: a new model with coupled GTP hydrolysis and multistep catastrophe

Hugo Bowne-Anderson et al. Bioessays. 2013 May.
Free PMC article

Erratum in

  • Bioessays. 2013 Jun;35(6):579

Abstract

A key question in understanding microtubule dynamics is how GTP hydrolysis leads to catastrophe, the switch from slow growth to rapid shrinkage. We first provide a review of the experimental and modeling literature, and then present a new model of microtubule dynamics. We demonstrate that vectorial, random, and coupled hydrolysis mechanisms are not consistent with the dependence of catastrophe on tubulin concentration and show that, although single-protofilament models can explain many features of dynamics, they do not describe catastrophe as a multistep process. Finally, we present a new combined (coupled plus random hydrolysis) multiple-protofilament model that is a simple, analytically solvable generalization of a single-protofilament model. This model accounts for the observed lifetimes of growing microtubules, the delay to catastrophe following dilution and describes catastrophe as a multistep process.

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Figures

Figure 1
Figure 1
Microtubule dynamic instability. Microtubules are 13-protofilament cylindrical polymers, which switch between phases of growth and shrinkage. Tubulin dimers are incorporated into the growing lattice in the GTP-bound form and stochastically hydrolyze to GDP-tubulin, thus forming a GTP-cap. It is thought that the switching from growth to shrinkage occurs due to the loss of the GTP-cap.
Figure 2
Figure 2
Timeline of milestones in modeling microtubule dynamics.
Figure 3
Figure 3
Measuring microtubule lengths and lifetimes. A: Kymograph made from a DIC movie, depicting typical microtubule growth and shrinkage using GMPCPP-stabilized microtubule seed and 12 µM tubulin. B: The measured length of a microtubule depends on the imaging technique used.
Figure 4
Figure 4
Schematic of single protofilament models. A: Vectorial hydrolysis, in which hydrolysis occurs only at the GDP-/GTP-tubulin-interface. B: Random hydrolysis, in which at any time, each GTP-tubulin dimer in the microtubule has the same probability of hydrolyzing. C: Coupled-random hydrolysis, in which the hydrolysis occurs randomly except that the terminal dimer cannot hydrolyze. D: The distinction between the stabilizing cap, the GTP-cap (the length of uninterrupted GTP-tubulin at the end), and the GTP-tubulin decay length, over which the fraction of GTP-tubulin drops e-fold.
Figure 5
Figure 5
Predictions of the main models alongside experimental data from Walker et al. and Gardner et al. . A: Vectorial hydrolysis with rates kon = 3.2 µM−1 s−1, k = 0.1 s−1, and h = 43.5 s−1. This value of h satisfies h > r − k ≍ 42 s. The data from Walker et al. are the points for which the authors had >10 observations. Error bars represent SE. B: Random hydrolysis: Simulation 1 rates are kon = 3.2 µM−1 s−1, k = 1 s−1, and h = 1.43 s−1; Simulation 2 rates are kon = 3.2 µM−1 s−1, k = 24 s−1, and h = 0.26 s−1. C: The multiple-protofilament coupled-random model fitted to 12 µM data from Gardner et al. . To do so, we used the mle MATLAB function. We did so for n = 2, 3, 4, with free parameter T′. The closest fit was given by n = 3 and a step time T′ = 1,580 ± 30 s. D: Analytic solution to the multiple-protofilament coupled-random model (Equation 2a), fitted to the lifetime data from Gardner et al. and the dilution data from Walker et al. (solid line); kon = 3.2 µM−1 s−1, k = 0.2 s−1, h = 0.12 s−1. Microtubules are not observed to nucleate below 5 µM. Our model predicts lifetimes of microtubules when diluted to concentrations above 0 µM and below the critical concentration for growth (dashed line). The arrow points to the predictions for dilution experiments to 0 µM in the model.
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