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The size of the EB cap determines instantaneous microtubule stability.

Duellberg C, Cade NI, Holmes D, Surrey T - Elife (2016)

Bottom Line: Using a microfluidics-assisted multi-colour TIRF microscopy assay with close-to-nm and sub-second precision, we measured the sizes of the stabilizing cap of individual microtubules.Nevertheless, the trigger of instability lies in a short region at the end of the cap, as a quantitative model of cap stability demonstrates.Our study establishes the spatial and kinetic characteristics of the protective cap and provides an insight into the molecular mechanism by which its loss leads to the switch from microtubule growth to shrinkage.

View Article: PubMed Central - PubMed

Affiliation: Lincoln's Inn Fields Laboratory, The Francis Crick Institute, London, United Kingdom.

ABSTRACT
The function of microtubules relies on their ability to switch between phases of growth and shrinkage. A nucleotide-dependent stabilising cap at microtubule ends is thought to be lost before this switch can occur; however, the nature and size of this protective cap are unknown. Using a microfluidics-assisted multi-colour TIRF microscopy assay with close-to-nm and sub-second precision, we measured the sizes of the stabilizing cap of individual microtubules. We find that the protective caps are formed by the extended binding regions of EB proteins. Cap lengths vary considerably and longer caps are more stable. Nevertheless, the trigger of instability lies in a short region at the end of the cap, as a quantitative model of cap stability demonstrates. Our study establishes the spatial and kinetic characteristics of the protective cap and provides an insight into the molecular mechanism by which its loss leads to the switch from microtubule growth to shrinkage.

No MeSH data available.


Related in: MedlinePlus

Threshold model fits considering 2-step maturation of the microtubule end.(A) Averaged delay times as a function of microtubule growth speed as shown in Figure 6A (tubulin concentration variation dataset) are fitted using either an 'end density' or 'total number' threshold model, either assuming simple 1-step maturation of microtubule end regions, as throughout this study, or assuming 2-step maturation kinetics (Maurer et al., 2014), as indicated. Both end density models yield good fits, predicting thresholds of 0.20 and 0.25 for the 1-step and 2-step maturation model, respectively. Both 'total number' models yielded unsatisfactory fits. (B) Averaged delay times as shown in Figure 6C (Mal3 dataset and its control) are fitted using an 'end density' threshold model either assuming 1-step or 2-step microtubule end maturation, as indicated. Predicted thresholds: Mal3 data – 0.28 and 0.29 for the 1-step and 2-step model, respectively; control without Mal3 – 0.14 and 0.17 for the 1-step and 2-step model, respectively. For the fits of the data without Mal3, k1 = 5km; for the fits of the data with Mal3, k1 = 20km (Maurer et al., 2014). The other fit parameters were as in Figure 6A and 6C.DOI:http://dx.doi.org/10.7554/eLife.13470.018
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fig6s3: Threshold model fits considering 2-step maturation of the microtubule end.(A) Averaged delay times as a function of microtubule growth speed as shown in Figure 6A (tubulin concentration variation dataset) are fitted using either an 'end density' or 'total number' threshold model, either assuming simple 1-step maturation of microtubule end regions, as throughout this study, or assuming 2-step maturation kinetics (Maurer et al., 2014), as indicated. Both end density models yield good fits, predicting thresholds of 0.20 and 0.25 for the 1-step and 2-step maturation model, respectively. Both 'total number' models yielded unsatisfactory fits. (B) Averaged delay times as shown in Figure 6C (Mal3 dataset and its control) are fitted using an 'end density' threshold model either assuming 1-step or 2-step microtubule end maturation, as indicated. Predicted thresholds: Mal3 data – 0.28 and 0.29 for the 1-step and 2-step model, respectively; control without Mal3 – 0.14 and 0.17 for the 1-step and 2-step model, respectively. For the fits of the data without Mal3, k1 = 5km; for the fits of the data with Mal3, k1 = 20km (Maurer et al., 2014). The other fit parameters were as in Figure 6A and 6C.DOI:http://dx.doi.org/10.7554/eLife.13470.018

Mentions: For simplicity we have assumed here throughout that the EB binding site region starts directly at the microtubule end (one-step end maturation). Previously, a detailed analysis of fluorescent EB end profiles has revealed an additional small non-binding region at the very end of the microtubule before the actual EB binding region (Maurer et al., 2014). This region could be accounted for by a two-step end maturation process consisting first of fast generation of EB binding sites, and a subsequent slower maturation into lattice sites. Applying this more complex model here did not improve the quality of the fits (Figure 6—figure supplement 3) and confirmed that the threshold of stability is defined by a critical end density of cap sites and not by a critical total number of cap sites (Figure 6—figure supplement 3A). In the absence of Mal3, the 2-step maturation model predicted threshold values for the end density that were ~25% larger compared to the simpler 1-step model. In the presence of Mal3 the two models predicted the same threshold value (within error) due to the first maturation step being very fast in the presence of EB1 family proteins (Maurer et al., 2014). Therefore, the conceptually simpler end maturation model is sufficient to capture the basic principles determining momentary microtubule stability, especially for the more physiological condition in the presence of an EB1 family protein.


The size of the EB cap determines instantaneous microtubule stability.

Duellberg C, Cade NI, Holmes D, Surrey T - Elife (2016)

Threshold model fits considering 2-step maturation of the microtubule end.(A) Averaged delay times as a function of microtubule growth speed as shown in Figure 6A (tubulin concentration variation dataset) are fitted using either an 'end density' or 'total number' threshold model, either assuming simple 1-step maturation of microtubule end regions, as throughout this study, or assuming 2-step maturation kinetics (Maurer et al., 2014), as indicated. Both end density models yield good fits, predicting thresholds of 0.20 and 0.25 for the 1-step and 2-step maturation model, respectively. Both 'total number' models yielded unsatisfactory fits. (B) Averaged delay times as shown in Figure 6C (Mal3 dataset and its control) are fitted using an 'end density' threshold model either assuming 1-step or 2-step microtubule end maturation, as indicated. Predicted thresholds: Mal3 data – 0.28 and 0.29 for the 1-step and 2-step model, respectively; control without Mal3 – 0.14 and 0.17 for the 1-step and 2-step model, respectively. For the fits of the data without Mal3, k1 = 5km; for the fits of the data with Mal3, k1 = 20km (Maurer et al., 2014). The other fit parameters were as in Figure 6A and 6C.DOI:http://dx.doi.org/10.7554/eLife.13470.018
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fig6s3: Threshold model fits considering 2-step maturation of the microtubule end.(A) Averaged delay times as a function of microtubule growth speed as shown in Figure 6A (tubulin concentration variation dataset) are fitted using either an 'end density' or 'total number' threshold model, either assuming simple 1-step maturation of microtubule end regions, as throughout this study, or assuming 2-step maturation kinetics (Maurer et al., 2014), as indicated. Both end density models yield good fits, predicting thresholds of 0.20 and 0.25 for the 1-step and 2-step maturation model, respectively. Both 'total number' models yielded unsatisfactory fits. (B) Averaged delay times as shown in Figure 6C (Mal3 dataset and its control) are fitted using an 'end density' threshold model either assuming 1-step or 2-step microtubule end maturation, as indicated. Predicted thresholds: Mal3 data – 0.28 and 0.29 for the 1-step and 2-step model, respectively; control without Mal3 – 0.14 and 0.17 for the 1-step and 2-step model, respectively. For the fits of the data without Mal3, k1 = 5km; for the fits of the data with Mal3, k1 = 20km (Maurer et al., 2014). The other fit parameters were as in Figure 6A and 6C.DOI:http://dx.doi.org/10.7554/eLife.13470.018
Mentions: For simplicity we have assumed here throughout that the EB binding site region starts directly at the microtubule end (one-step end maturation). Previously, a detailed analysis of fluorescent EB end profiles has revealed an additional small non-binding region at the very end of the microtubule before the actual EB binding region (Maurer et al., 2014). This region could be accounted for by a two-step end maturation process consisting first of fast generation of EB binding sites, and a subsequent slower maturation into lattice sites. Applying this more complex model here did not improve the quality of the fits (Figure 6—figure supplement 3) and confirmed that the threshold of stability is defined by a critical end density of cap sites and not by a critical total number of cap sites (Figure 6—figure supplement 3A). In the absence of Mal3, the 2-step maturation model predicted threshold values for the end density that were ~25% larger compared to the simpler 1-step model. In the presence of Mal3 the two models predicted the same threshold value (within error) due to the first maturation step being very fast in the presence of EB1 family proteins (Maurer et al., 2014). Therefore, the conceptually simpler end maturation model is sufficient to capture the basic principles determining momentary microtubule stability, especially for the more physiological condition in the presence of an EB1 family protein.

Bottom Line: Using a microfluidics-assisted multi-colour TIRF microscopy assay with close-to-nm and sub-second precision, we measured the sizes of the stabilizing cap of individual microtubules.Nevertheless, the trigger of instability lies in a short region at the end of the cap, as a quantitative model of cap stability demonstrates.Our study establishes the spatial and kinetic characteristics of the protective cap and provides an insight into the molecular mechanism by which its loss leads to the switch from microtubule growth to shrinkage.

View Article: PubMed Central - PubMed

Affiliation: Lincoln's Inn Fields Laboratory, The Francis Crick Institute, London, United Kingdom.

ABSTRACT
The function of microtubules relies on their ability to switch between phases of growth and shrinkage. A nucleotide-dependent stabilising cap at microtubule ends is thought to be lost before this switch can occur; however, the nature and size of this protective cap are unknown. Using a microfluidics-assisted multi-colour TIRF microscopy assay with close-to-nm and sub-second precision, we measured the sizes of the stabilizing cap of individual microtubules. We find that the protective caps are formed by the extended binding regions of EB proteins. Cap lengths vary considerably and longer caps are more stable. Nevertheless, the trigger of instability lies in a short region at the end of the cap, as a quantitative model of cap stability demonstrates. Our study establishes the spatial and kinetic characteristics of the protective cap and provides an insight into the molecular mechanism by which its loss leads to the switch from microtubule growth to shrinkage.

No MeSH data available.


Related in: MedlinePlus