http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc/

Autophagy is a pivotal cytoprotective process that secures cellular homeostasis, fulfills essential roles in development, immunity and defence against pathogens, and determines the lifespan of eukaryotic organisms. However, autophagy also crucially contributes to the development of age-related human pathologies, including cancer and neurodegeneration. Macroautophagy (hereafter referred to as autophagy) clears the cytoplasm by stochastic or specific cargo recognition and destruction, and is initiated and executed by autophagy related (ATG) proteins functioning in dynamical hierarchies to form autophagosomes. Autophagosomes sequester cytoplasmic cargo material, including proteins, lipids and organelles, and acquire acidic hydrolases from the lysosomal compartment for cargo degradation. Prerequisite and essential for autophagosome formation is the production of phosphatidylinositol 3-phosphate (PtdIns3P) by phosphatidylinositol 3-kinase class III (PI3KC3, also known as PIK3C3) in complex with beclin 1, p150 (also known as PIK3R4; Vps15 in yeast) and ATG14L. Members of the human WDrepeat protein interacting with phosphoinositides (WIPI) family play an important role in recognizing and decoding the PtdIns3P signal at the nascent autophagosome, and hence function as autophagy-specific PtdIns3P-binding effectors, similar to their ancestral yeast Atg18 homolog. The PtdIns3P effector function of human WIPI proteins appears to be compromised in cancer and neurodegeneration, and WIPI genes and proteins might present novel targets for rational therapies. Here, we summarize the current knowledge on the roles of the four human WIPI proteins, WIPI1-4, in autophagy.This article is part of a Focus on Autophagosome biogenesis. For further reading, please see related articles: 'ERES: sites for autophagosome biogenesis and maturation?' by Jana SanchezWandelmer et al. (J. Cell Sci. 128,(185)(186)(187)(188)(189)(190)(191)(192) and 'Membrane dynamics in autophagosome biogenesis' by Sven R. Carlsson and Anne Simonsen (J. Cell Sci. 128,(193)(194)(195)(196)(197)(198)(199)(200)(201)(202)(203)(204)(205).

Cell-based models of the blood-brain barrier (BBB) are important for increasing the knowledge of BBB formation, degradation and brain exposure of drug substances. Human models are preferred over animal models because of interspecies differences in BBB structure and function. However, access to human primary BBB tissue is limited and has shown degeneration of BBB functions in vitro. Human induced pluripotent stem cells (iPSCs) can be used to generate relevant cell types to model the BBB with human tissue. We generated a human iPSC-derived model of the BBB that includes endothelial cells in coculture with pericytes, astrocytes and neurons. Evaluation of barrier properties showed that the endothelial cells in our coculture model have high transendothelial electrical resistance, functional efflux and ability to discriminate between CNS permeable and non-permeable substances. Whole genome expression profiling revealed transcriptional changes that occur in coculture, including upregulation of tight junction proteins, such as claudins and neurotransmitter transporters. Pathway analysis implicated changes in the WNT, TNF, and PI3K-Akt pathways upon coculture. Our data suggest that coculture of iPSC-derived endothelial cells promotes barrier formation on a functional and transcriptional level. The information about gene expression changes in coculture can be used to further improve iPSC-derived BBB models through selective pathway manipulation. Stem Cells 2018.

Motivation: Knowing the localization of a protein within the cell helps elucidate its role in biological processes, its function and its potential as a drug target. Thus, subcellular localization prediction is an active research area. Numerous localization prediction systems are described in the literature; some focus on specific localizations or organisms, while others attempt to cover a wide range of localizations. Results: We introduce SherLoc, a new comprehensive system for predicting the localization of eukaryotic proteins. It integrates several types of sequence and text-based features. While applying the widely used support vector machines (SVMs), SherLoc's main novelty lies in the way in which it selects its text sources and features, and integrates those with sequence-based features. We test SherLoc on previously used datasets, as well as on a new set devised specifically to test its predictive power, and show that SherLoc consistently improves on previous reported results. We also report the results of applying SherLoc to a large set of yetunlocalized proteins.