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      Lysosomal enzyme trafficking factor LYSET enables nutritional usage of extracellular proteins

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          Abstract

          Mammalian cells can generate amino acids through macropinocytosis and lysosomal breakdown of extracellular proteins, which is exploited by cancer cells to grow in nutrient-poor tumors. Here, through genetic screens in defined nutrient conditions we characterized LYSET, a transmembrane protein (TMEM251) selectively required when cells consume extracellular proteins. LYSET was found to associate in the Golgi with GlcNAc-1-phosphotransferase, which targets catabolic enzymes to lysosomes through mannose-6-phosphate modification. Without LYSET, GlcNAc-1-phosphotransferase was unstable owing to a hydrophilic transmembrane domain. Consequently, LYSET-deficient cells were depleted of lysosomal enzymes and impaired in turnover of macropinocytic and autophagic cargoes. Thus, LYSET represents a core component of the lysosomal enzyme trafficking pathway, underlies the pathomechanism for hereditary lysosomal storage disorders, and may represent a target to suppress metabolic adaptations in cancer.

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            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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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                September 08 2022
                Affiliations
                [1 ]Cell Signaling and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany.
                [2 ]Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany.
                [3 ]Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.
                [4 ]Vienna BioCenter PhD Program, Doctoral School of the University at Vienna and Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria.
                [5 ]Center for Molecular Biology of Heidelberg University (ZMBH), Heidelberg, Germany.
                [6 ]MS-based Protein Analysis Unit, Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Heidelberg, Germany.
                [7 ]Electron Microscopy Facility, Vienna BioCenter Core Facilities GmbH, Vienna, Austria.
                [8 ]Core Facility Tumor Models, German Cancer Research Center (DKFZ), Heidelberg, Germany.
                [9 ]Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany.
                [10 ]Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany.
                [11 ]Center for Biochemistry and Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Faculty of Medicine, University of Cologne, Cologne, Germany.
                [12 ]Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria.
                Article
                10.1126/science.abn5637
                36074822
                878d6ca8-c82d-43c2-a632-cfe3fa8e892b
                © 2022
                History

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