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Interplay between phosphorylation and palmitoylation mediates plasma membrane targeting and sorting of GAP43.

Gauthier-Kemper A, Igaev M, Sündermann F, Janning D, Brühmann J, Moschner K, Reyher HJ, Junge W, Glebov K, Walter J, Bakota L, Brandt R - Mol. Biol. Cell (2014)

Bottom Line: Plasma membrane association decreased the diffusion constant fourfold in neuritic shafts.Simulations confirmed that a combination of diffusion, dynamic plasma membrane interaction and active transport of a small fraction of GAP43 suffices for efficient sorting to growth cones.Our data demonstrate a complex interplay between phosphorylation and lipidation in mediating the localization of GAP43 in neuronal cells.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, University of Osnabrück, 49076 Osnabrück, Germany.

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Simulation of kinetic curves for sorting of proteins at different conditions. (A) Table showing the parameters used for the simulations. We used values that we experimentally determined in this study. (B) Schematic outline of the model. Technical details are given in Materials and Methods. (C) Simulated kinetic curves for sorting of the proteins at different conditions using the values given in A and the model shown in B. Note that a combination of diffusion, phosphorylation-regulated membrane reaction, and transport of a small fraction (10%) of protein with the speed of fast axonal transport suffices to simulate the experimental data for sorting of GAP43wt (blue lines in C vs. Figure 6B).
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Figure 8: Simulation of kinetic curves for sorting of proteins at different conditions. (A) Table showing the parameters used for the simulations. We used values that we experimentally determined in this study. (B) Schematic outline of the model. Technical details are given in Materials and Methods. (C) Simulated kinetic curves for sorting of the proteins at different conditions using the values given in A and the model shown in B. Note that a combination of diffusion, phosphorylation-regulated membrane reaction, and transport of a small fraction (10%) of protein with the speed of fast axonal transport suffices to simulate the experimental data for sorting of GAP43wt (blue lines in C vs. Figure 6B).

Mentions: Our experimental data provided evidence that the distribution of GAP43 is guided by diffusion, phosphorylation-mediated plasma membrane association, and transport. To dissect the contribution of the different mechanisms in sorting of GAP43 to the growth cone, we developed a simulation based on three factors: 1) diffusion in the plasma membrane; 2) membrane reactions, that is, phosphorylation-mediated exchange between plasma membrane–bound and cytoplasmic protein; and 3) axonal transport of a fraction of the protein (Figure 8, A and B). The simulation showed that diffusion in the plasma membrane with a value that we had experimentally determined for PAGFP-F (∼0.4 μm2/s) as sole mechanism of distribution results in a high time constant for appearance in the growth cone. Such behavior is in agreement with the distribution of PAGFP-F as a plasma membrane–associated reference protein (red curve in Figure 8C and respective curve in Figure 6B). Inclusion of the occurrence of membrane reactions with a pseudoequilibrium constant similar to what we observed for GAP43wt (Kd* = 1 [50% bound]) and diffusion of the cytosolic component with a value that we had experimentally determined for 3×PAGFP resulted in the reduction of τ by a factor of approximately four (Figure 8C, dashed blue line). Addition of a transport component led to a further decrease of τ by a factor of two, resulting in kinetics similar to the behavior of GAP43wt (blue curve in Figure 8C and respective curve in Figure 6B). Remarkably, a fraction of only 10% of the molecules undergoing fast axonal transport was sufficient to simulate the experimental data for sorting of GAP43.


Interplay between phosphorylation and palmitoylation mediates plasma membrane targeting and sorting of GAP43.

Gauthier-Kemper A, Igaev M, Sündermann F, Janning D, Brühmann J, Moschner K, Reyher HJ, Junge W, Glebov K, Walter J, Bakota L, Brandt R - Mol. Biol. Cell (2014)

Simulation of kinetic curves for sorting of proteins at different conditions. (A) Table showing the parameters used for the simulations. We used values that we experimentally determined in this study. (B) Schematic outline of the model. Technical details are given in Materials and Methods. (C) Simulated kinetic curves for sorting of the proteins at different conditions using the values given in A and the model shown in B. Note that a combination of diffusion, phosphorylation-regulated membrane reaction, and transport of a small fraction (10%) of protein with the speed of fast axonal transport suffices to simulate the experimental data for sorting of GAP43wt (blue lines in C vs. Figure 6B).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4214776&req=5

Figure 8: Simulation of kinetic curves for sorting of proteins at different conditions. (A) Table showing the parameters used for the simulations. We used values that we experimentally determined in this study. (B) Schematic outline of the model. Technical details are given in Materials and Methods. (C) Simulated kinetic curves for sorting of the proteins at different conditions using the values given in A and the model shown in B. Note that a combination of diffusion, phosphorylation-regulated membrane reaction, and transport of a small fraction (10%) of protein with the speed of fast axonal transport suffices to simulate the experimental data for sorting of GAP43wt (blue lines in C vs. Figure 6B).
Mentions: Our experimental data provided evidence that the distribution of GAP43 is guided by diffusion, phosphorylation-mediated plasma membrane association, and transport. To dissect the contribution of the different mechanisms in sorting of GAP43 to the growth cone, we developed a simulation based on three factors: 1) diffusion in the plasma membrane; 2) membrane reactions, that is, phosphorylation-mediated exchange between plasma membrane–bound and cytoplasmic protein; and 3) axonal transport of a fraction of the protein (Figure 8, A and B). The simulation showed that diffusion in the plasma membrane with a value that we had experimentally determined for PAGFP-F (∼0.4 μm2/s) as sole mechanism of distribution results in a high time constant for appearance in the growth cone. Such behavior is in agreement with the distribution of PAGFP-F as a plasma membrane–associated reference protein (red curve in Figure 8C and respective curve in Figure 6B). Inclusion of the occurrence of membrane reactions with a pseudoequilibrium constant similar to what we observed for GAP43wt (Kd* = 1 [50% bound]) and diffusion of the cytosolic component with a value that we had experimentally determined for 3×PAGFP resulted in the reduction of τ by a factor of approximately four (Figure 8C, dashed blue line). Addition of a transport component led to a further decrease of τ by a factor of two, resulting in kinetics similar to the behavior of GAP43wt (blue curve in Figure 8C and respective curve in Figure 6B). Remarkably, a fraction of only 10% of the molecules undergoing fast axonal transport was sufficient to simulate the experimental data for sorting of GAP43.

Bottom Line: Plasma membrane association decreased the diffusion constant fourfold in neuritic shafts.Simulations confirmed that a combination of diffusion, dynamic plasma membrane interaction and active transport of a small fraction of GAP43 suffices for efficient sorting to growth cones.Our data demonstrate a complex interplay between phosphorylation and lipidation in mediating the localization of GAP43 in neuronal cells.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, University of Osnabrück, 49076 Osnabrück, Germany.

Show MeSH
Related in: MedlinePlus