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      Role of FAM134 paralogues in endoplasmic reticulum remodeling, ER‐phagy, and Collagen quality control

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          Abstract

          Degradation of the endoplasmic reticulum (ER) via selective autophagy (ER‐phagy) is vital for cellular homeostasis. We identify FAM134A/RETREG2 and FAM134C/RETREG3 as ER‐phagy receptors, which predominantly exist in an inactive state under basal conditions. Upon autophagy induction and ER stress signal, they can induce significant ER fragmentation and subsequent lysosomal degradation. FAM134A, FAM134B/RETREG1, and FAM134C are essential for maintaining ER morphology in a LC3‐interacting region (LIR)‐dependent manner. Overexpression of any FAM134 paralogue has the capacity to significantly augment the general ER‐phagy flux upon starvation or ER‐stress. Global proteomic analysis of FAM134 overexpressing and knockout cell lines reveals several protein clusters that are distinctly regulated by each of the FAM134 paralogues as well as a cluster of commonly regulated ER‐resident proteins. Utilizing pro‐Collagen I, as a shared ER‐phagy substrate, we observe that FAM134A acts in a LIR‐independent manner and compensates for the loss of FAM134B and FAM134C, respectively. FAM134C instead is unable to compensate for the loss of its paralogues. Taken together, our data show that FAM134 paralogues contribute to common and unique ER‐phagy pathways.

          Abstract

          Selective degradation of the ER is essential to maintain ER homeostasis. This study characterizes FAM134A and FAM134C as ER‐phagy receptors involved in Collagen quality control and identifies protein clusters differentially regulated by FAM134 paralogues.

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          Most cited references58

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Comparative protein modelling by satisfaction of spatial restraints.

            We describe a comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equivalent residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain C alpha-C alpha distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the molecular pdf such that the model violates the input restraints as little as possible. The molecular pdf is derived as a combination of pdfs restraining individual spatial features of the whole molecule. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modelling of trypsin from two other serine proteinases.
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              GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit.

              Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. GROMACS is an open source and free software available from http://www.gromacs.org. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                p.grumati@tigem.it
                stolz@em.uni-frankfurt.de
                Journal
                EMBO Rep
                EMBO Rep
                10.1002/(ISSN)1469-3178
                EMBR
                embor
                EMBO Reports
                John Wiley and Sons Inc. (Hoboken )
                1469-221X
                1469-3178
                02 August 2021
                06 September 2021
                02 August 2021
                : 22
                : 9 ( doiID: 10.1002/embr.v22.9 )
                : e52289
                Affiliations
                [ 1 ] Telethon Institute of Genetics and Medicine (TIGEM) Pozzuoli Italy
                [ 2 ] Institute of Biochemistry II (IBC2) Faculty of Medicine Goethe University Frankfurt am Main Germany
                [ 3 ] Buchmann Institute for Molecular Life Sciences (BMLS) Goethe University Frankfurt am Main Germany
                [ 4 ] Department of Theoretical Biophysics Max Planck Institute of Biophysics Frankfurt am Main Germany
                [ 5 ] Structural Genomics Consortium at BMLS Goethe University Frankfurt am Main Germany
                [ 6 ] Institute of Human Genetics Jena University Hospital Friedrich‐Schiller‐University Jena Germany
                [ 7 ] Institute for Biophysics Goethe University Frankfurt am Main Germany
                Author notes
                [*] [* ] Corresponding author. Tel: +39 081 1923 0688; E‐mail: p.grumati@ 123456tigem.it

                Corresponding author. Tel: +49 069 798 42589; E‐mail: stolz@ 123456em.uni-frankfurt.de

                [ † ]

                These authors contributed equally to this work

                Author information
                https://orcid.org/0000-0001-5333-7502
                https://orcid.org/0000-0002-7742-0391
                https://orcid.org/0000-0002-5829-8589
                https://orcid.org/0000-0001-7768-746X
                https://orcid.org/0000-0002-9942-9389
                https://orcid.org/0000-0002-3340-439X
                Article
                EMBR202052289
                10.15252/embr.202052289
                8447607
                34338405
                1491891c-e0b9-45f1-b911-230fe7ff53c1
                © 2021 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
                : 07 July 2021
                : 16 December 2020
                : 08 July 2021
                Page count
                Figures: 14, Tables: 0, Pages: 20, Words: 14484
                Funding
                Funded by: Fondazione Telethon (Telethon Foundation) , doi 10.13039/501100002426;
                Award ID: TMPGCBX16TT
                Funded by: Fondazione Roche
                Award ID: Roche per la Ricerca 2019
                Funded by: AFM‐Telethon
                Award ID: Trampoline Grant 2020
                Funded by: Fondazione Umberto Veronesi (Umberto Veronesi Foundation) , doi 10.13039/501100004710;
                Funded by: Else Kröner‐Fresenius‐Stiftung (EKFS) , doi 10.13039/501100003042;
                Award ID: 2016_A196
                Funded by: Innovative Medicines Initiative (IMI) , doi 10.13039/501100010767;
                Award ID: EUbOPEN grant n° 875510
                Funded by: Max Planck Institut, Frankfurt
                Funded by: Deutsche Forschungsgemeinschaft (DFG) , doi 10.13039/501100001659;
                Award ID: HU 800/6‐2
                Award ID: RTG 1715
                Award ID: ID 259130777
                Award ID: SFB 1177
                Categories
                Article
                Articles
                Custom metadata
                2.0
                06 September 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.7 mode:remove_FC converted:06.09.2021

                Molecular biology
                autophagy,collagen,er stress,er‐phagy,fam134,autophagy & cell death,organelles
                Molecular biology
                autophagy, collagen, er stress, er‐phagy, fam134, autophagy & cell death, organelles

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