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      FBXL4 suppresses mitophagy by restricting the accumulation of NIX and BNIP3 mitophagy receptors

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          Abstract

          To maintain both mitochondrial quality and quantity, cells selectively remove damaged or excessive mitochondria through mitophagy, which is a specialised form of autophagy. Mitophagy is induced in response to diverse conditions, including hypoxia, cellular differentiation and mitochondrial damage. However, the mechanisms that govern the removal of specific dysfunctional mitochondria under steady‐state conditions to fine‐tune mitochondrial content are not well understood. Here, we report that SCF FBXL4, an SKP1/CUL1/F‐box protein ubiquitin ligase complex, localises to the mitochondrial outer membrane in unstressed cells and mediates the constitutive ubiquitylation and degradation of the mitophagy receptors NIX and BNIP3 to suppress basal levels of mitophagy. We demonstrate that the pathogenic variants of FBXL4 that cause encephalopathic mtDNA depletion syndrome (MTDPS13) do not efficiently interact with the core SCF ubiquitin ligase machinery or mediate the degradation of NIX and BNIP3. Thus, we reveal a molecular mechanism whereby FBXL4 actively suppresses mitophagy by preventing NIX and BNIP3 accumulation. We propose that the dysregulation of NIX and BNIP3 turnover causes excessive basal mitophagy in FBXL4‐associated mtDNA depletion syndrome.

          Abstract

          Steady‐state ubiquitylation and turnover of NIX and BNIP3 limits basal mitophagy and is dysregulated in MTDPS13, accounting for the elevated mitophagy associated with this encephalopathic mtDNA depletion syndrome.

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          Most cited references52

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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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            ColabFold: making protein folding accessible to all

            ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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              HIFalpha targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing.

              HIF (hypoxia-inducible factor) is a transcription factor that plays a pivotal role in cellular adaptation to changes in oxygen availability. In the presence of oxygen, HIF is targeted for destruction by an E3 ubiquitin ligase containing the von Hippel-Lindau tumor suppressor protein (pVHL). We found that human pVHL binds to a short HIF-derived peptide when a conserved proline residue at the core of this peptide is hydroxylated. Because proline hydroxylation requires molecular oxygen and Fe(2+), this protein modification may play a key role in mammalian oxygen sensing.
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                Author and article information

                Contributors
                j.pagan@uq.edu.au
                Journal
                EMBO J
                EMBO J
                10.1002/(ISSN)1460-2075
                EMBJ
                embojnl
                The EMBO Journal
                John Wiley and Sons Inc. (Hoboken )
                0261-4189
                1460-2075
                10 May 2023
                July 2023
                10 May 2023
                : 42
                : 13 ( doiID: 10.1002/embj.v42.13 )
                : e112767
                Affiliations
                [ 1 ] Faculty of Medicine, School of Biomedical Sciences University of Queensland Brisbane QLD Australia
                [ 2 ] Department of Biotechnology, School of Biotechnology Viet Nam National University‐International University Ho Chi Minh City Vietnam
                [ 3 ] Department of Biochemistry and Molecular Pharmacology New York University Grossman School of Medicine New York NY USA
                [ 4 ] Perlmutter Cancer Center New York University Grossman School of Medicine New York NY USA
                [ 5 ] Department of Pathology and Lab Medicine, Meyer Cancer Center Weill Cornell Medicine New York NY USA
                [ 6 ] Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute The University of Queensland Brisbane QLD Australia
                [ 7 ] Institute for Molecular Bioscience The University of Queensland Brisbane QLD Australia
                [ 8 ] Centre for Microscopy and Microanalysis University of Queensland Brisbane QLD Australia
                [ 9 ] Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia
                [ 10 ] Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute Monash University Melbourne VIC Australia
                [ 11 ] Department of Medical Biology University of Melbourne Melbourne VIC Australia
                [ 12 ] Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences Newcastle University Newcastle upon Tyne UK
                [ 13 ] NHS Highly Specialised Service for Rare Mitochondrial Disorders Newcastle upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne UK
                [ 14 ] The University of Queensland Diamantina Institute, Faculty of Medicine The University of Queensland Brisbane QLD Australia
                [ 15 ] School of Chemistry & Molecular Biosciences University of Queensland Brisbane QLD Australia
                Author notes
                [*] [* ]Corresponding author. Tel: +61 7 0427985780; E‐mail: j.pagan@ 123456uq.edu.au
                [ † ]

                These authors contributed equally to this work

                Author information
                https://orcid.org/0000-0002-3136-0485
                https://orcid.org/0000-0001-5162-4807
                https://orcid.org/0000-0002-0960-8237
                https://orcid.org/0000-0002-7494-5248
                https://orcid.org/0000-0002-7768-8873
                https://orcid.org/0000-0002-6070-3774
                https://orcid.org/0000-0002-7079-6388
                Article
                EMBJ2022112767
                10.15252/embj.2022112767
                10308361
                37161784
                65d01ca6-7389-4899-8efb-09eddb087d5e
                © 2023 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 April 2023
                : 11 October 2022
                : 20 April 2023
                Page count
                Figures: 10, Tables: 0, Pages: 22, Words: 14872
                Funding
                Funded by: American‐Italian Cancer Foundation (AICF) , doi 10.13039/100005410;
                Funded by: Brain Foundation Research Grant
                Funded by: Department of Education and Training|Australian Research Council (ARC)
                Award ID: FT180100172
                Award ID: DP210102704
                Award ID: DP200100347
                Funded by: DHAC|National Health and Medical Research Council (NHMRC) , doi 10.13039/501100000925;
                Award ID: APP1183915
                Award ID: APP1136021
                Award ID: APP2016410
                Award ID: APP1140064
                Award ID: APP1150083
                Award ID: APP1156489
                Award ID: GNT1106471
                Funded by: Fondazione AIRC per la ricerca sul cancro ETS (AIRC)
                Funded by: HHS|NIH|National Cancer Institute (NCI)
                Award ID: T32CA009161
                Funded by: Lily Foundation (Lily)
                Funded by: Medical Research Council Centre for Translational Research in Neuromuscular Disease Mitochondrial Disease Patient Cohort (UK) , doi 10.13039/100007472;
                Award ID: G0800674
                Funded by: Mito Foundation (Mito) UK
                Funded by: Mito Foundation Incubator Grant
                Funded by: Newcastle University|Wellcome Trust Centre for Mitochondrial Research (WCMR)
                Award ID: 203105/Z/16/Z
                Funded by: Newcastle upon Tyne Hospitals NHS Foundation Trust (Newcastle upon Tyne Hospitals NHS Trust) , doi 10.13039/501100003776;
                Funded by: Stafford Fox
                Funded by: The Pathologic Society
                Funded by: UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and Children
                Funded by: UKRI|Medical Research Council (MRC)
                Award ID: MR/W019027/1
                Award ID: MR/S005021/1
                Categories
                Article
                Articles
                Custom metadata
                2.0
                03 July 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.0 mode:remove_FC converted:29.06.2023

                Molecular biology
                bnip3,fbxl4,mitochondria,mitophagy,nix/bnip3l,autophagy & cell death,organelles,post-translational modifications & proteolysis

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