10 research outputs found

    Glycosphingolipid synthesis requires FAPP2 transfer of glucosylceramide

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    The molecular machinery responsible for the generation of transport carriers moving from the Golgi complex to the plasma membrane relies on a tight interplay between proteins and lipids. Among the lipid-binding proteins of this machinery, we previously identified the four-phosphate adaptor protein FAPP2, the pleckstrin homology domain of which binds phosphatidylinositol 4-phosphate and the small GTPase ARF1. FAPP2 also possesses a glycolipid-transfer-protein homology domain. Here we show that human FAPP2 is a glucosylceramide-transfer protein that has a pivotal role in the synthesis of complex glycosphingolipids, key structural and signalling components of the plasma membrane. The requirement for FAPP2 makes the whole glycosphingolipid synthetic pathway sensitive to regulation by phosphatidylinositol 4-phosphate and ARF1. Thus, by coupling the synthesis of glycosphingolipids with their export to the cell surface, FAPP2 emerges as crucial in determining the lipid identity and composition of the plasma membrane

    Exiting the Golgi complex

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    The composition and identity of cell organelles are dictated by the flux of lipids and proteins that they receive and lose through cytosolic exchange and membrane trafficking. The trans-Golgi network (TGN) is a major sorting centre for cell lipids and proteins at the crossroads of the endocytic and exocytic pathways; it has a complex dynamic structure composed of a network of tubular membranes that generate pleiomorphic carriers targeted to different destinations. Live-cell imaging combined with three-dimensional tomography has recently provided the temporal and topographical framework that allows the assembly of the numerous molecular machineries so far implicated in sorting and trafficking at the TGN

    Universal fragment descriptors for predicting properties of inorganic crystals

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    Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules

    Exiting the Golgi complex

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