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      Cytosolic adaptation to mitochondria-induced proteostatic stress causes progressive muscle wasting

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          Summary

          Mitochondrial dysfunction causes muscle wasting in many diseases and probably also during aging. The underlying mechanism is poorly understood. We generated transgenic mice with unbalanced mitochondrial protein loading and import, by moderately overexpressing the nuclear-encoded adenine nucleotide translocase, Ant1. We found that these mice progressively lose skeletal muscle. Ant1-overloading reduces mitochondrial respiration. Interestingly, it also induces small heat shock proteins and aggresome-like structures in the cytosol, suggesting increased proteostatic burden due to accumulation of unimported mitochondrial preproteins. The transcriptome of Ant1-transgenic muscles is drastically remodeled to counteract proteostatic stress, by repressing protein synthesis and promoting proteasomal function, autophagy, and lysosomal amplification. These proteostatic adaptations collectively reduce protein content thereby reducing myofiber size and muscle mass. Thus, muscle wasting can occur as a trade-off of adaptation to mitochondria-induced proteostatic stress. This finding could have implications for understanding the mechanism of muscle wasting, especially in diseases associated with Ant1 overexpression, including facioscapulohumeral dystrophy.

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          Highlights

          • Ant1 overexpression causes progressive muscle wasting without affecting lifespan

          • ANT1 overloading saturates the mitochondrial protein import pathway to cause mPOS

          • Muscle responds to mPOS to repress the synthesis and increase turnover of proteins

          • Chronic adaptation to mPOS reduces myofiber size and muscle mass as a trade-off

          Abstract

          Biological sciences; Cellular physiology; Cell biology; Functional aspects of cell biology

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

            We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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              voom: precision weights unlock linear model analysis tools for RNA-seq read counts

              New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                31 December 2021
                21 January 2022
                31 December 2021
                : 25
                : 1
                : 103715
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, State University of New York Upstate Medical University, Syracuse, NY 13210, USA
                [2 ]Department of Neuroscience and Physiology, State University of New York Upstate Medical University, Syracuse, NY 13210, USA
                [3 ]Department of Neurology, University of Rochester, Rochester, NY 14642, USA
                Author notes
                []Corresponding author Chenx@ 123456upstate.edu
                [4]

                Lead contact

                Article
                S2589-0042(21)01685-0 103715
                10.1016/j.isci.2021.103715
                8762400
                35072007
                b2fc2086-8412-41e4-9884-ec2d040bab6f
                © 2021 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 7 April 2021
                : 15 October 2021
                : 29 December 2021
                Categories
                Article

                biological sciences,cellular physiology,cell biology,functional aspects of cell biology

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