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      Dual function of GTPBP6 in biogenesis and recycling of human mitochondrial ribosomes

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          Abstract

          Translation and ribosome biogenesis in mitochondria require auxiliary factors that ensure rapid and accurate synthesis of mitochondrial proteins. Defects in translation are associated with oxidative phosphorylation deficiency and cause severe human diseases, but the exact roles of mitochondrial translation-associated factors are not known. Here we identify the functions of GTPBP6, a homolog of the bacterial ribosome-recycling factor HflX, in human mitochondria. Similarly to HflX, GTPBP6 facilitates the dissociation of ribosomes in vitro and in vivo. In contrast to HflX, GTPBP6 is also required for the assembly of mitochondrial ribosomes. GTPBP6 ablation leads to accumulation of late assembly intermediate(s) of the large ribosomal subunit containing ribosome biogenesis factors MTERF4, NSUN4, MALSU1 and the GTPases GTPBP5, GTPBP7 and GTPBP10. Our data show that GTPBP6 has a dual function acting in ribosome recycling and biogenesis. These findings contribute to our understanding of large ribosomal subunit assembly as well as ribosome recycling pathway in mitochondria.

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            The Perseus computational platform for comprehensive analysis of (prote)omics data.

            A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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              Dynamic light scattering: a practical guide and applications in biomedical sciences.

              Dynamic light scattering (DLS), also known as photon correlation spectroscopy (PCS), is a very powerful tool for studying the diffusion behaviour of macromolecules in solution. The diffusion coefficient, and hence the hydrodynamic radii calculated from it, depends on the size and shape of macromolecules. In this review, we provide evidence of the usefulness of DLS to study the homogeneity of proteins, nucleic acids, and complexes of protein-protein or protein-nucleic acid preparations, as well as to study protein-small molecule interactions. Further, we provide examples of DLS's application both as a complementary method to analytical ultracentrifugation studies and as a screening tool to validate solution scattering models using determined hydrodynamic radii.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                16 December 2020
                02 December 2020
                02 December 2020
                : 48
                : 22
                : 12929-12942
                Affiliations
                Department of Cellular Biochemistry, University Medical Center Goettingen , D-37073 Goettingen, Germany
                Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells’ (MBExC), University of Goettingen , Goettingen, Germany
                Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells’ (MBExC), University of Goettingen , Goettingen, Germany
                Department of Physical Biochemistry, Max Planck Institute for Biophysical Chemistry , D-37077 Goettingen, Germany
                Department of Cellular Biochemistry, University Medical Center Goettingen , D-37073 Goettingen, Germany
                Department of Cellular Biochemistry, University Medical Center Goettingen , D-37073 Goettingen, Germany
                Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry , D-37077 Goettingen, Germany
                Bioanalytics, Institute for Clinical Chemistry, University Medical Center Goettingen , D-37073 Goettingen, Germany
                Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry , D-37077 Goettingen, Germany
                Bioanalytics, Institute for Clinical Chemistry, University Medical Center Goettingen , D-37073 Goettingen, Germany
                Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells’ (MBExC), University of Goettingen , Goettingen, Germany
                Department of Physical Biochemistry, Max Planck Institute for Biophysical Chemistry , D-37077 Goettingen, Germany
                Department of Cellular Biochemistry, University Medical Center Goettingen , D-37073 Goettingen, Germany
                Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells’ (MBExC), University of Goettingen , Goettingen, Germany
                Author notes
                To whom correspondence should be addressed. Tel: +49 551 395913; Fax: +49 551 395979; Email: ricarda.richter@ 123456med.uni-goettingen.de
                Author information
                http://orcid.org/0000-0001-8496-9721
                http://orcid.org/0000-0003-0105-3879
                http://orcid.org/0000-0001-8904-1543
                Article
                gkaa1132
                10.1093/nar/gkaa1132
                7736812
                33264405
                5ff30c5f-6c20-4213-bd32-78ad3a8aa12f
                © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 09 November 2020
                : 19 October 2020
                : 29 July 2020
                Page count
                Pages: 14
                Funding
                Funded by: Deutsche Forschungsgemeinschaft, DOI 10.13039/501100001659;
                Award ID: RI 2715/1-1
                Funded by: Excellence Cluster;
                Award ID: EXC 2067/1-390729940
                Funded by: Collaborative Research Center;
                Award ID: SFB860
                Funded by: Max Planck Society, DOI 10.13039/501100004189;
                Categories
                AcademicSubjects/SCI00010
                RNA and RNA-protein complexes

                Genetics
                Genetics

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