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First published online 11 December 2007
doi: 10.1242/jcs.013383

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A model for the self-organization of exit sites in the endoplasmic reticulum

¹ Cellular Biophysics Group (BIOMS), German Cancer Research Center, Bioquant Center, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany
² Biomedical Computer Vision Group, Department of Bioinformatics and Functional Genomics, IPMB, University of Heidelberg, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany
³ German Cancer Research Center, D-69120 Heidelberg, Germany

View larger version (56K): [in this window] [in a new window]   Fig. 1. Sketch of the model for the spatiotemporal evolution of ERES. A basic event in the simulation is the attachment of particles (e.g. COPII complexes) from the cytoplasm (blue) to the ER membrane (red), either to any place on the membrane (`free binding') or with a preference to patches near to pre-existing clusters (`biased binding'). While on the membrane, particles diffuse and are able to fuse to form larger clusters when touching each other. Particles can also detach from the membrane after hydrolysis of GTP. Upon reaching a certain size, clusters can pinch off vesicles (green). See main text and Materials and Methods for further details.

View larger version (41K): [in this window] [in a new window]   Fig. 2. Simulated ERES-like clusters keep their distance. (a) The steady state of the simulation obtained for physiological parameters (see Materials and Methods) yields a pattern of spots that is reminiscent of the ERES pattern in mammalian cells. (b) The statistics of normalized next-neighbor distances between visible clusters, p(s), obtained from the simulations (histogram), show marked deviations from the naïve prediction of clusters distributed randomly and independently (Eqn 1; dashed line). A good description of the data is given by an analytical expression derived within random matrix theory (Eqn 2, unbroken line). The pattern has quasi-crystalline characteristics.

View larger version (32K): [in this window] [in a new window]   Fig. 3. Depletion forces yield a quasi-crystalline arrangement of ERES. (a) Distributing ERES positions randomly and independently in a circular cell with radius 15 µm (inset: typical configuration) yields the anticipated agreement of the numerically determined p(s) (histogram) with Eqn 1 (shown dashed). Owing to the imposed hard-core radius (200 nm) of the ERES, very small distances (s<0.2) are not observed. (b) Reducing the probability to locate an ERES next to a pre-existing one with a Gaussian filter (see main text and Materials and Methods) yields an almost perfect agreement with the results from the model (Eqn 2; unbroken line).

View larger version (22K): [in this window] [in a new window]   Fig. 4. The sizes of ERES-like clusters in the simulation. The mean size <n> of ERES-like clusters decreases upon accelerating the binding kinetics (e.g. by depleting transport-competent cargo) in the case of biased binding (A) and free binding (B). Here, Kon was increased while keeping Koff=Kon/2, Kon, 2Kon (light grey, dark grey, black fill). Using biased binding – that is, a cooperativity of COPII complexes – the ERES-like clusters were significantly larger (compare B with A). (C) The distribution of normalized sizes, p(a) with a=n/<n> always showed a broad shape with a cut-off that is imposed by the smallest visible cluster.

View larger version (47K): [in this window] [in a new window]   Fig. 5. Spatial arrangement of ERES in vivo. The distribution of (normalized) next-neighbor distances, p(s), between ERES shows marked deviations from the assumption of randomly and independently positioned spots (Eqn 1; dashed line) in untreated (A) and CHX-treated (B) cells. In particular, small and large distances are suppressed, in agreement with the predictions of the model. Insets show representative fluorescence images of transfected CHO cells.

View larger version (25K): [in this window] [in a new window]   Fig. 6. Cargo-dependent size distribution and mobility of ERES. (a) Distribution of normalized sizes of ERES, p(a), in untreated (blue) and CHX-treated (red) CHO cells. The experimental data (histograms) are best described by a lognormal distribution (Eqn 3; colored dashed lines), indicating strong deviations of the individual ERES size from the mean <a>=1. Treatment with CHX increased the fraction of small ERES, meaning that the typical size (indicated by the peak of the distribution) shifted to smaller sizes. (b) The average diffusion coefficient D() of the ERES population, as determined by single-particle tracking, shows initially subdiffusion and converges towards an asymptotic value D0=const. (unbroken and dashed lines) that depends on the treatment. In general, addition of CHX resulted in a twofold enhancement of diffusion with and without treatment with NOC, in agreement with the observed larger portion of small-sized ERES.

View larger version (10K): [in this window] [in a new window]   Fig. 7. The number of ERES and rate of vesicle production in the model for varying COPII kinetics. While the number of ERES (NERES) increases for faster COPII turnover (Kon=Koff), the rate Rves at which vesicles are produced decreases. This result was the same when using the free or biased binding (black and grey, respectively). This observation indicates that growing the size of ERES is more advantageous to achieve a high secretory flux in comparison with increasing the number of ERES.