3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Direct control of lysosomal catabolic activity by mTORC1 through regulation of V-ATPase assembly

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Mammalian cells can acquire exogenous amino acids through endocytosis and lysosomal catabolism of extracellular proteins. In amino acid-replete environments, nutritional utilization of extracellular proteins is suppressed by the amino acid sensor mechanistic target of rapamycin complex 1 (mTORC1) through an unknown process. Here, we show that mTORC1 blocks lysosomal degradation of extracellular proteins by suppressing V-ATPase-mediated acidification of lysosomes. When mTORC1 is active, peripheral V-ATPase V 1 domains reside in the cytosol where they are stabilized by association with the chaperonin TRiC. Consequently, most lysosomes display low catabolic activity. When mTORC1 activity declines, V-ATPase V 1 domains move to membrane-integral V-ATPase V o domains at lysosomes to assemble active proton pumps. The resulting drop in luminal pH increases protease activity and degradation of protein contents throughout the lysosomal population. These results uncover a principle by which cells rapidly respond to changes in their nutrient environment by mobilizing the latent catabolic capacity of lysosomes.

          Abstract

          mTORC1 blocks lysosomal nutrient generation. Here, the authors show that mTORC1 inactivation triggers V-ATPase assembly, which rapidly initiates lysosomal acidification and degradation of protein contents throughout the lysosomal population.

          Related collections

          Most cited references58

          • Record: found
          • Abstract: not found
          • Article: not found

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

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Fiji: an open-source platform for biological-image analysis.

            Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              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.
                Bookmark

                Author and article information

                Contributors
                w.palm@dkfz-heidelberg.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 August 2022
                17 August 2022
                2022
                : 13
                : 4848
                Affiliations
                [1 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Cell Signaling and Metabolism, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [2 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Faculty of Biosciences, , University of Heidelberg, ; Heidelberg, Germany
                [3 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, MS-based Protein Analysis Unit, Genomics and Proteomics Core Facility, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                Author information
                http://orcid.org/0000-0002-4104-7556
                http://orcid.org/0000-0002-1101-2264
                http://orcid.org/0000-0001-9321-2069
                http://orcid.org/0000-0003-0612-770X
                Article
                32515
                10.1038/s41467-022-32515-6
                9385660
                35977928
                6faff774-303c-4190-bee4-a021cc6e4c98
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 December 2021
                : 3 August 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: PA 3679/2-1
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                nutrient signalling,lysosomes,proteases,proteomics
                Uncategorized
                nutrient signalling, lysosomes, proteases, proteomics

                Comments

                Comment on this article