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      A phosphoinositide signalling pathway mediates rapid lysosomal repair

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          Abstract

          Lysosomal dysfunction has been increasingly linked to disease and normal ageing 1, 2 . Lysosomal membrane permeabilization (LMP), a hallmark of lysosome-related diseases, can be triggered by diverse cellular stressors 3 . Given the damaging contents of lysosomes, LMP must be rapidly resolved, although the underlying mechanisms are poorly understood. Here, using an unbiased proteomic approach, we show that LMP stimulates a phosphoinositide-initiated membrane tethering and lipid transport (PITT) pathway for rapid lysosomal repair. Upon LMP, phosphatidylinositol-4 kinase type 2α (PI4K2A) accumulates rapidly on damaged lysosomes, generating high levels of the lipid messenger phosphatidylinositol-4-phosphate. Lysosomal phosphatidylinositol-4-phosphate in turn recruits multiple oxysterol-binding protein (OSBP)-related protein (ORP) family members, including ORP9, ORP10, ORP11 and OSBP, to orchestrate extensive new membrane contact sites between damaged lysosomes and the endoplasmic reticulum. The ORPs subsequently catalyse robust endoplasmic reticulum-to-lysosome transfer of phosphatidylserine and cholesterol to support rapid lysosomal repair. Finally, the lipid transfer protein ATG2 is also recruited to damaged lysosomes where its activity is potently stimulated by phosphatidylserine. Independent of macroautophagy, ATG2 mediates rapid membrane repair through direct lysosomal lipid transfer. Together, our findings identify that the PITT pathway maintains lysosomal membrane integrity, with important implications for numerous age-related diseases characterized by impaired lysosomal function.

          Abstract

          Lysosomal membrane damage triggers a lipid signalling pathway that repairs lysosomes via lipid transport at newly established endoplasmic reticulum–lysosomal membrane contact sites.

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          Most cited references55

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The PRIDE database and related tools and resources in 2019: improving support for quantification data

            Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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              AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

              The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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                Author and article information

                Contributors
                jay.tan@pitt.edu
                finkelt@pitt.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                7 September 2022
                : 1-7
                Affiliations
                [1 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Aging Institute, , University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, ; Pittsburgh, PA USA
                [2 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Department of Cell Biology, , University of Pittsburgh School of Medicine, ; Pittsburgh, PA USA
                [3 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Department of Medicine, , University of Pittsburgh School of Medicine, ; Pittsburgh, PA USA
                Author information
                http://orcid.org/0000-0003-3300-2240
                http://orcid.org/0000-0002-0726-3546
                Article
                5164
                10.1038/s41586-022-05164-4
                9450835
                36071159
                8cafb78e-4b8f-4977-b9b4-98a57b8dc403
                © The Author(s), under exclusive licence to Springer Nature Limited 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 18 November 2020
                : 29 July 2022
                Categories
                Article

                Uncategorized
                lysosomes,cell signalling,fluorescent proteins,phospholipids,endoplasmic reticulum
                Uncategorized
                lysosomes, cell signalling, fluorescent proteins, phospholipids, endoplasmic reticulum

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