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

      Ubiquitinome Profiling Reveals in Vivo UBE2D3 Targets and Implicates UBE2D3 in Protein Quality Control

      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

          Ubiquitination has crucial roles in many cellular processes, and dysregulation of ubiquitin machinery enzymes can result in various forms of pathogenesis. Cells only have a limited set of ubiquitin-conjugating (E2) enzymes to support the ubiquitination of many cellular targets. As individual E2 enzymes have many different substrates and interactions between E2 enzymes and their substrates can be transient, it is challenging to define all in vivo substrates of an individual E2 and the cellular processes it affects. Particularly challenging in this respect is UBE2D3, an E2 enzyme with promiscuous activity in vitro but less defined roles in vivo. Here, we set out to identify in vivo targets of UBE2D3 by using stable isotope labeling by amino acids in cell culture–based and label-free quantitative ubiquitin diGly proteomics to study global proteome and ubiquitinome changes associated with UBE2D3 depletion. UBE2D3 depletion changed the global proteome, with the levels of proteins from metabolic pathways, in particular retinol metabolism, being the most affected. However, the impact of UBE2D3 depletion on the ubiquitinome was much more prominent. Interestingly, molecular pathways related to mRNA translation were the most affected. Indeed, we find that ubiquitination of the ribosomal proteins RPS10 and RPS20, critical for ribosome-associated protein quality control, is dependent on UBE2D3. We show by Targets of Ubiquitin Ligases Identified by Proteomics 2 methodology that RPS10 and RPS20 are direct targets of UBE2D3 and demonstrate that the catalytic activity of UBE2D3 is required to ubiquitinate RPS10 in vivo. In addition, our data suggest that UBE2D3 acts at multiple levels in autophagic protein quality control. Collectively, our findings show that depletion of an E2 enzyme in combination with quantitative diGly-based ubiquitinome profiling is a powerful tool to identify new in vivo E2 substrates, as we have done here for UBE2D3. Our work provides an important resource for further studies on the in vivo functions of UBE2D3.

          Graphical Abstract

          Highlights

          • SILAC-based and label-free diGly proteomics to identify in vivo substrates of UBE2D3.

          • UBE2D3 affects CRABP1 and TSPAN8 protein levels.

          • UBE2D3 regulates ubiquitination of proteins involved in protein quality control.

          • UBE2D3 ubiquitinates RPS10 and RPS20 in vivo.

          In Brief

          To identify in vivo substrates of the E2 ubiquitin-conjugating enzyme UBE2D3, we combined SILAC-based and label-free diGly-proteomics with UBE2D3 depletion. We show that UBE2D3 impacts CRABP1 and TSPAN8 levels and ubiquitinates key proteins in protein quality control. TULIP2 methodology identifies RPS10, RPS20, and SQSTM1 among direct targets of UBE2D3, and we show that UBE2D3's catalytic activity is required for RPS10 ubiquitination in vivo. Our approach identifies many potential UBE2D3 substrates, thereby providing a resource for future studies on UBE2D3 functions.

          Related collections

          Most cited references92

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The PRIDE database and related tools and resources in 2019: improving support for quantification data

            Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Contributors
                Journal
                Mol Cell Proteomics
                Mol Cell Proteomics
                Molecular & Cellular Proteomics : MCP
                American Society for Biochemistry and Molecular Biology
                1535-9476
                1535-9484
                13 April 2023
                June 2023
                13 April 2023
                : 22
                : 6
                : 100548
                Affiliations
                [1 ]Division of Oncogenomics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
                [2 ]Proteomics Center, Erasmus Medical Center, Rotterdam, The Netherlands
                [3 ]Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
                [4 ]Proteomics Facility, The Netherlands Cancer Institute, Amsterdam, The Netherlands
                [5 ]Genome Proteomics Laboratory, Andalusian Center for Molecular Biology and Regenerative Medicine (CABIMER), University of Seville, Seville, Spain
                [6 ]Department of Cell Biology, University of Seville, Seville, Spain
                [7 ]Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Netherlands Proteomics Center, University of Utrecht, Utrecht, The Netherlands
                Author notes
                []For correspondence: Jacqueline J.L. Jacobs j.jacobs@ 123456nki.nl
                [‡]

                These authors contributed equally to this work.

                Article
                S1535-9476(23)00058-0 100548
                10.1016/j.mcpro.2023.100548
                10209342
                d010f61b-7c91-494f-b793-f4f4f99d1f44
                © 2023 The Authors

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

                History
                : 19 July 2022
                : 29 March 2023
                Categories
                Research

                Molecular biology
                ube2d3,digly-proteomics,pqc,rps10,silac
                Molecular biology
                ube2d3, digly-proteomics, pqc, rps10, silac

                Comments

                Comment on this article