Abstract:Fluorescence recovery after photobleaching measurements with high spatial resolution are performed to elucidate the impact of the actin cytoskeleton on translational mobility of green fluorescent protein (GFP) in aqueous domains of Dictyostelium discoideum amoebae. In vegetative Dictyostelium cells, GFP molecules experience a 3.6-fold reduction of their translational mobility relative to dilute aqueous solutions. In disrupting the actin filamentous network using latrunculin-A, the intact actin cytoskeletal net… Show more
“…The algorithm provides as an output the distribution of the obtained apparent diffusion constants for a specific condition. To test the robustness of the algorithm, we used free eGFP diffusing in aqueous solution, for which we obtained a single peak distribution centred around 92 Όm 2 /s, a value which is in agreement with previous studies (Potma et al , 2001). …”
Section: Methodssupporting
confidence: 84%
“…As expected, the average autocorrelation curve fits well with a oneâcomponent free diffusion model (Fig 4A). The single peak of the frequency distribution of diffusion constants, generated by the Maximum Entropy approach (Materials and Methods), was centred around 92 Όm 2 /s (Fig 4C), as previously described (Potma et al , 2001). Next, we analysed transiently expressed eGFP in living cells, whose average autocorrelation curve also fitted a oneâcomponent free diffusion model (Fig 4B), as expected for a protein with no specific interactions with the nuclear environment.…”
SAGA and ATAC are two distinct chromatin modifying coâactivator complexes with distinct enzymatic activities involved in RNA polymerase II (Pol II) transcription regulation. To investigate the mobility of coâactivator complexes and general transcription factors in liveâcell nuclei, we performed imaging experiments based on photobleaching. SAGA and ATAC, but also two general transcription factors (TFIID and TFIIB), were highly dynamic, exhibiting mainly transient associations with chromatin, contrary to Pol II, which formed more stable chromatin interactions. Fluorescence correlation spectroscopy analyses revealed that the mobile pool of the two coâactivators, as well as that of TFIID and TFIIB, can be subdivided into âfastâ (free) and âslowâ (chromatinâinteracting) populations. Inhibiting transcription elongation decreased H3K4 trimethylation and reduced the âslowâ population of SAGA, ATAC, TFIIB and TFIID. In addition, inhibiting histone H3K4 trimethylation also reduced the âslowâ populations of SAGA and ATAC. Thus, our results demonstrate that in the nuclei of live cells the equilibrium between fast and slow population of SAGA or ATAC complexes is regulated by active transcription via changes in the abundance of H3K4me3 on chromatin.
“…The algorithm provides as an output the distribution of the obtained apparent diffusion constants for a specific condition. To test the robustness of the algorithm, we used free eGFP diffusing in aqueous solution, for which we obtained a single peak distribution centred around 92 Όm 2 /s, a value which is in agreement with previous studies (Potma et al , 2001). …”
Section: Methodssupporting
confidence: 84%
“…As expected, the average autocorrelation curve fits well with a oneâcomponent free diffusion model (Fig 4A). The single peak of the frequency distribution of diffusion constants, generated by the Maximum Entropy approach (Materials and Methods), was centred around 92 Όm 2 /s (Fig 4C), as previously described (Potma et al , 2001). Next, we analysed transiently expressed eGFP in living cells, whose average autocorrelation curve also fitted a oneâcomponent free diffusion model (Fig 4B), as expected for a protein with no specific interactions with the nuclear environment.…”
SAGA and ATAC are two distinct chromatin modifying coâactivator complexes with distinct enzymatic activities involved in RNA polymerase II (Pol II) transcription regulation. To investigate the mobility of coâactivator complexes and general transcription factors in liveâcell nuclei, we performed imaging experiments based on photobleaching. SAGA and ATAC, but also two general transcription factors (TFIID and TFIIB), were highly dynamic, exhibiting mainly transient associations with chromatin, contrary to Pol II, which formed more stable chromatin interactions. Fluorescence correlation spectroscopy analyses revealed that the mobile pool of the two coâactivators, as well as that of TFIID and TFIIB, can be subdivided into âfastâ (free) and âslowâ (chromatinâinteracting) populations. Inhibiting transcription elongation decreased H3K4 trimethylation and reduced the âslowâ population of SAGA, ATAC, TFIIB and TFIID. In addition, inhibiting histone H3K4 trimethylation also reduced the âslowâ populations of SAGA and ATAC. Thus, our results demonstrate that in the nuclei of live cells the equilibrium between fast and slow population of SAGA or ATAC complexes is regulated by active transcription via changes in the abundance of H3K4me3 on chromatin.
“…2). Although the axonemal components will reduce the effective cross-sectional area available for diffusion, as the maximal diameter of each microtubule The aqueous diffusion coefficient of EGFP, D aq, EGFP , was reported previously to be 87 (Swaminathan et al, 1997;Brown et al, 1999;Potma et al, 2001) and 91 ”m 2 s îČ1 (Calvert et al, 2007). D aq, PAGFP was reported to be 89 ”m 2 s îČ1 (Calvert et al, 2007).…”
“…A great deal of information about motion of molecules in living cells has been obtained from intracellular measurements using different experimental techniques 13,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] and from simulations 14,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] . Experimental data are usually obtained by fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS) techniques.…”
Abstract. Particle diffusion in crowded media was studied through Monte Carlo simulations in 3D obstructed lattices. Three particular aspects affecting the diffusion, not extensively treated in three-dimensional geometry, were analysed: the relative particle-obstacle size, the relative particle-obstacle mobility and the way of having the obstacles distributed in the simulation space (randomly or uniformly). The results are interpreted in terms of the parameters that characterize the time dependence of the diffusion coefficient: the anomalous diffusion exponent (a), the crossover time from anomalous to normal diffusion regimes (Ï) and the long time diffusion coefficient (D*). Simulation results indicate that there is a more anomalous diffusion (smaller a) and lower long time diffusion coefficient (D*) when obstacle concentration increases, and that, for a given total excluded volume and immobile obstacles, the anomalous diffusion effect is less important for bigger size obstacles. However, for the case of mobile obstacles, this size effect is inverted yielding values that are in qualitatively good agreement with in vitro experiments of protein diffusion in crowded media.These results underline that the pattern of the spatial partitioning of the obstacleexcluded volume is a factor to be considered together with the value of the excluded volume itself.
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