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      Spartin-mediated lipid transfer facilitates lipid droplet turnover

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          Significance

          The Troyer syndrome protein spartin was proposed to function as a lipophagy receptor that delivers lipid droplets, organelles key for energy storage and membrane lipid homeostasis, to autophagosomes for degradation. We identify an additional function for spartin as a lipid transfer protein and show its transfer ability is required for lipid droplet degradation, including by lipophagy. Our data support that protein-mediated lipid transfer plays a role in lipid droplet turnover. Moreover, in spartin’s senescence domain, we have characterized a lipid transport module that likely also features in still undiscovered aspects of lipid droplet biology and membrane homeostasis.

          Abstract

          Lipid droplets (LDs) are organelles critical for energy storage and membrane lipid homeostasis, whose number and size are carefully regulated in response to cellular conditions. The molecular mechanisms underlying lipid droplet biogenesis and degradation, however, are not well understood. The Troyer syndrome protein spartin (SPG20) supports LD delivery to autophagosomes for turnover via lipophagy. Here, we characterize spartin as a lipid transfer protein whose transfer ability is required for LD degradation. Spartin copurifies with phospholipids and neutral lipids from cells and transfers phospholipids in vitro via its senescence domain. A senescence domain truncation that impairs lipid transfer in vitro also impairs LD turnover in cells while not affecting spartin association with either LDs or autophagosomes, supporting that spartin’s lipid transfer ability is physiologically relevant. Our data indicate a role for spartin-mediated lipid transfer in LD turnover.

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          Most cited references37

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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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            Genome engineering using the CRISPR-Cas9 system.

            Targeted nucleases are powerful tools for mediating genome alteration with high precision. The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA. Here we describe a set of tools for Cas9-mediated genome editing via nonhomologous end joining (NHEJ) or homology-directed repair (HDR) in mammalian cells, as well as generation of modified cell lines for downstream functional studies. To minimize off-target cleavage, we further describe a double-nicking strategy using the Cas9 nickase mutant with paired guide RNAs. This protocol provides experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. Beginning with target design, gene modifications can be achieved within as little as 1-2 weeks, and modified clonal cell lines can be derived within 2-3 weeks.
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              The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

              The PRoteomics IDEntifications (PRIDE) database ( https://www.ebi.ac.uk/pride/ ) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. 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 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                8 January 2024
                16 January 2024
                8 July 2024
                : 121
                : 3
                : e2314093121
                Affiliations
                [1] aDepartment of Cell Biology, Yale University School of Medicine , New Haven, CT 06520
                [2] bDepartment of Biochemistry and Microbiology, University of Victoria , Victoria, BC V8W2Y2, Canada
                [3] cDepartment of Biochemistry and Molecular Biology, University of British Columbia , Vancouver, BC V6T 1Z3, Canada
                Author notes
                1To whom correspondence may be addressed. Email: karin.reinisch@ 123456yale.edu .

                Edited by Tom Rapoport, Harvard University, Boston, MA; received August 15, 2023; accepted December 1, 2023

                Author information
                https://orcid.org/0000-0001-6094-2305
                https://orcid.org/0000-0002-8513-3288
                https://orcid.org/0000-0001-6270-559X
                https://orcid.org/0000-0001-7904-9859
                https://orcid.org/0000-0002-5798-4624
                https://orcid.org/0000-0001-9140-6150
                Article
                202314093
                10.1073/pnas.2314093121
                10801920
                38190532
                3baf5cab-a7a8-4473-93e3-3716aaad97c9
                Copyright © 2024 the Author(s). Published by PNAS.

                This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 15 August 2023
                : 01 December 2023
                Page count
                Pages: 10, Words: 9015
                Funding
                Funded by: HHS | NIH | National Institute of General Medical Sciences (NIGMS), FundRef 100000057;
                Award ID: R35GM131715
                Award Recipient : Karin M. Reinisch Award Recipient : Thomas J. Melia
                Funded by: HHS | NIH | National Institute of General Medical Sciences (NIGMS), FundRef 100000057;
                Award ID: R01135GM135290
                Award Recipient : Karin M. Reinisch Award Recipient : Thomas J. Melia
                Funded by: Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC), FundRef 501100000038;
                Award ID: Discovery Grant 2020-04241
                Award Recipient : John E. Burke
                Categories
                research-article, Research Article
                cell-bio, Cell Biology
                409
                Biological Sciences
                Cell Biology

                lipid droplet turnover,lipid transport protein,membrane dynamics

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