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      MitoStores: chaperone‐controlled protein granules store mitochondrial precursors in the cytosol

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          Abstract

          Hundreds of nucleus‐encoded mitochondrial precursor proteins are synthesized in the cytosol and imported into mitochondria in a post‐translational manner. However, the early processes associated with mitochondrial protein targeting remain poorly understood. Here, we show that in Saccharomyces cerevisiae, the cytosol has the capacity to transiently store mitochondrial matrix‐destined precursors in dedicated deposits that we termed MitoStores. Competitive inhibition of mitochondrial protein import via clogging of import sites greatly enhances the formation of MitoStores, but they also form during physiological cell growth on nonfermentable carbon sources. MitoStores are enriched for a specific subset of nucleus‐encoded mitochondrial proteins, in particular those containing N‐terminal mitochondrial targeting sequences. Our results suggest that MitoStore formation suppresses the toxic potential of aberrantly accumulating mitochondrial precursor proteins and is controlled by the heat shock proteins Hsp42 and Hsp104. Thus, the cytosolic protein quality control system plays an active role during the early stages of mitochondrial protein targeting through the coordinated and localized sequestration of mitochondrial precursor proteins.

          Abstract

          Transient storage in newly identified cytosolic granules protects yeast cells from the toxic effects of excess mitochondrial matrix‐destined precursors.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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                Author and article information

                Contributors
                hannes.herrmann@biologie.uni-kl.de
                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
                27 January 2023
                April 2023
                27 January 2023
                : 42
                : 7 ( doiID: 10.1002/embj.v42.7 )
                : e112309
                Affiliations
                [ 1 ] Cell Biology University of Kaiserslautern Kaiserslautern Germany
                [ 2 ] Molecular Genetics University of Kaiserslautern Kaiserslautern Germany
                [ 3 ] Zoology and Neurobiology University of Kaiserslautern Kaiserslautern Germany
                [ 4 ]Present address: Cellular Biochemistry Max Planck Institute of Biochemistry Martinsried Germany
                [ 5 ]Present address: Department of Genetics Stanford University Stanford CA USA
                Author notes
                [*] [* ]Corresponding author. Tel: +49 6312052406; E‐mail: hannes.herrmann@ 123456biologie.uni-kl.de
                Author information
                https://orcid.org/0000-0002-6737-7203
                https://orcid.org/0000-0003-2376-7047
                https://orcid.org/0000-0002-5115-5884
                https://orcid.org/0000-0003-2081-4506
                Article
                EMBJ2022112309
                10.15252/embj.2022112309
                10068336
                36704946
                31e003b3-8eb4-4b65-bc99-8bcd79e3ed82
                © 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
                : 05 January 2023
                : 05 August 2022
                : 12 January 2023
                Page count
                Figures: 13, Tables: 0, Pages: 25, Words: 15968
                Funding
                Funded by: Damon Runyon Cancer Research Foundation (DRCRF) , doi 10.13039/100001021;
                Award ID: DRG‐2461‐22
                Funded by: Deutsche Forschungsgemeinschaft (DFG) , doi 10.13039/501100001659;
                Award ID: GRK2737
                Award ID: HE2803/10‐1
                Funded by: Forschungsinitiative Rheinland‐Pfalz
                Award ID: Biocomp
                Funded by: Joachim Herz Stiftung (Joachim Herz Foundation) , doi 10.13039/100008662;
                Categories
                Article
                Articles
                Custom metadata
                2.0
                3 April 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:03.04.2023

                Molecular biology
                chaperones,mitochondria,proteasome,protein aggregates,protein translocation,organelles,translation & protein quality

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