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Self-organization of an acentrosomal microtubule network at the basal cortex of polarized epithelial cells.

Reilein A, Yamada S, Nelson WJ - J. Cell Biol. (2005)

Bottom Line: Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network.However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ.These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Beckman Center for Molecular and Genetic Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.

ABSTRACT
Mechanisms underlying the organization of centrosome-derived microtubule arrays are well understood, but less is known about how acentrosomal microtubule networks are formed. The basal cortex of polarized epithelial cells contains a microtubule network of mixed polarity. We examined how this network is organized by imaging microtubule dynamics in acentrosomal basal cytoplasts derived from these cells. We show that the steady-state microtubule network appears to form by a combination of microtubule-microtubule and microtubule-cortex interactions, both of which increase microtubule stability. We used computational modeling to determine whether these microtubule parameters are sufficient to generate a steady-state acentrosomal microtubule network. Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network. However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ. These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

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Stochastic simulation of microtubule network formation. (A) Simulated microtubule dynamics with or without plus end stabilization. Stabilization was simulated by an increase in rescue frequency when the plus end encountered another microtubule (MT–MT) or randomly distributed cortical spots (MT–cortex). Microtubules form a steady-state pattern in the presence of either microtubule–microtubule or microtubule–cortex interactions. Blue lines represent microtubules; red circles represent cortical stabilization points. See Videos 7 and 8 (available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). (B) The mean lengths of microtubules in each simulation (n = 10 simulations). (C) Formation of steady-state networks was measured by the correlation between two time steps separated by Δt = 2 min (see Materials and methods for details). Symbols are the same as in B. Background correlation coefficients (squares) were calculated between two unrelated simulations. Correlation coefficients are highest for simulated microtubule networks with stabilization points. (D) The effect of increasing the number of cortical stabilization points on the correlation coefficient. Δt = 2 min. Error bars represent SD.
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fig7: Stochastic simulation of microtubule network formation. (A) Simulated microtubule dynamics with or without plus end stabilization. Stabilization was simulated by an increase in rescue frequency when the plus end encountered another microtubule (MT–MT) or randomly distributed cortical spots (MT–cortex). Microtubules form a steady-state pattern in the presence of either microtubule–microtubule or microtubule–cortex interactions. Blue lines represent microtubules; red circles represent cortical stabilization points. See Videos 7 and 8 (available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). (B) The mean lengths of microtubules in each simulation (n = 10 simulations). (C) Formation of steady-state networks was measured by the correlation between two time steps separated by Δt = 2 min (see Materials and methods for details). Symbols are the same as in B. Background correlation coefficients (squares) were calculated between two unrelated simulations. Correlation coefficients are highest for simulated microtubule networks with stabilization points. (D) The effect of increasing the number of cortical stabilization points on the correlation coefficient. Δt = 2 min. Error bars represent SD.

Mentions: We first tested whether the simulated assembly of 30 microtubules undergoing dynamic instability as the only parameter could lead to the formation of a steady-state structure. In this test, one end of the microtubule was modeled to undergo dynamic instability (designated the plus end), whereas the other end was kept inert (designated the minus end). This simulation resulted in a distribution of microtubules (mean length 2.3 ± 0.4 μm) in a 5 × 5–μm space that exhibited continuous remodeling as a result of the disappearance of microtubules by depolymerization and spontaneous nucleation of microtubules elsewhere at random in the space (Fig. 7 A).


Self-organization of an acentrosomal microtubule network at the basal cortex of polarized epithelial cells.

Reilein A, Yamada S, Nelson WJ - J. Cell Biol. (2005)

Stochastic simulation of microtubule network formation. (A) Simulated microtubule dynamics with or without plus end stabilization. Stabilization was simulated by an increase in rescue frequency when the plus end encountered another microtubule (MT–MT) or randomly distributed cortical spots (MT–cortex). Microtubules form a steady-state pattern in the presence of either microtubule–microtubule or microtubule–cortex interactions. Blue lines represent microtubules; red circles represent cortical stabilization points. See Videos 7 and 8 (available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). (B) The mean lengths of microtubules in each simulation (n = 10 simulations). (C) Formation of steady-state networks was measured by the correlation between two time steps separated by Δt = 2 min (see Materials and methods for details). Symbols are the same as in B. Background correlation coefficients (squares) were calculated between two unrelated simulations. Correlation coefficients are highest for simulated microtubule networks with stabilization points. (D) The effect of increasing the number of cortical stabilization points on the correlation coefficient. Δt = 2 min. Error bars represent SD.
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Related In: Results  -  Collection

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fig7: Stochastic simulation of microtubule network formation. (A) Simulated microtubule dynamics with or without plus end stabilization. Stabilization was simulated by an increase in rescue frequency when the plus end encountered another microtubule (MT–MT) or randomly distributed cortical spots (MT–cortex). Microtubules form a steady-state pattern in the presence of either microtubule–microtubule or microtubule–cortex interactions. Blue lines represent microtubules; red circles represent cortical stabilization points. See Videos 7 and 8 (available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). (B) The mean lengths of microtubules in each simulation (n = 10 simulations). (C) Formation of steady-state networks was measured by the correlation between two time steps separated by Δt = 2 min (see Materials and methods for details). Symbols are the same as in B. Background correlation coefficients (squares) were calculated between two unrelated simulations. Correlation coefficients are highest for simulated microtubule networks with stabilization points. (D) The effect of increasing the number of cortical stabilization points on the correlation coefficient. Δt = 2 min. Error bars represent SD.
Mentions: We first tested whether the simulated assembly of 30 microtubules undergoing dynamic instability as the only parameter could lead to the formation of a steady-state structure. In this test, one end of the microtubule was modeled to undergo dynamic instability (designated the plus end), whereas the other end was kept inert (designated the minus end). This simulation resulted in a distribution of microtubules (mean length 2.3 ± 0.4 μm) in a 5 × 5–μm space that exhibited continuous remodeling as a result of the disappearance of microtubules by depolymerization and spontaneous nucleation of microtubules elsewhere at random in the space (Fig. 7 A).

Bottom Line: Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network.However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ.These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Beckman Center for Molecular and Genetic Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.

ABSTRACT
Mechanisms underlying the organization of centrosome-derived microtubule arrays are well understood, but less is known about how acentrosomal microtubule networks are formed. The basal cortex of polarized epithelial cells contains a microtubule network of mixed polarity. We examined how this network is organized by imaging microtubule dynamics in acentrosomal basal cytoplasts derived from these cells. We show that the steady-state microtubule network appears to form by a combination of microtubule-microtubule and microtubule-cortex interactions, both of which increase microtubule stability. We used computational modeling to determine whether these microtubule parameters are sufficient to generate a steady-state acentrosomal microtubule network. Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network. However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ. These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

Show MeSH
Related in: MedlinePlus