Spatiotemporal model of a key step in endocytosis: SNX9 recruitment via phosphoinositides
Auteurs : Johannes Schöneberg, Alexander Ullrich, York Posor, Volker Haucke, Frank Noe
Résumé : Clathrin mediated endocytosis (CME) is an ubiquitous cellular pathway that regulates central aspects of cell physiology such as nutrient uptake, modulation of signal transduction, synaptic transmission and membrane turn-over. Endocytic vesicle formation depends on the timed production of specific phosphoinositides and their interactions with various endocytic proteins. Recently, it has been found that phosphatidylinositol-3,4-bisphosphate (PI(3,4)P2) produced by the class II phosphatidylinositol 3-kinase C2alpha plays a key role in the recruitment of the PX-BAR domain protein SNX9, which is proposed to play a role in the constriction of the endocytic vesicle neck [Posor et al, Nature 499, p233 (2013)]. Interestingly, SNX9 and its close paralog SNX18 are not fully specific to PI(3,4)P2 but can also bind other phospholipids, in particular to PI(4,5)P2, an abundant plasma membrane lipid required for the recruitment of many endocytic proteins. In order to understand the dynamical interplay between phospholipids and endocytic proteins, we developed a computational model of the temporal changes in the population of the phosphoinositide-associated endocytic proteins and their spatial distribution at a clathrin-coated pit (CCP). The model resolves single molecules in time and space, and incorporates the complex interplay of proteins and lipids, as well as their movement within the CCP. We find that the comparably small differences in lipid binding affinities of endocytic proteins are amplified by competition among them, allowing for the selective enrichment of SNX9 at late stage CCPs as a result of timed PI(3,4)P2 production.
Explorez l'arbre d'article
Cliquez sur les nœuds de l'arborescence pour être redirigé vers un article donné et accéder à leurs résumés et assistant virtuel
Recherchez des articles similaires (en version bêta)
En cliquant sur le bouton ci-dessus, notre algorithme analysera tous les articles de notre base de données pour trouver le plus proche en fonction du contenu des articles complets et pas seulement des métadonnées. Veuillez noter que cela ne fonctionne que pour les articles pour lesquels nous avons généré des résumés et que vous pouvez le réexécuter de temps en temps pour obtenir un résultat plus précis pendant que notre base de données s'agrandit.