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      Simple But Efficacious Enrichment of Integral Membrane Proteins and Their Interactions for In-Depth Membrane Proteomics

      research-article
      1 , 2 , 1 , 2 , 3 , 2 , 3 , 1 , 2 ,
      Molecular & Cellular Proteomics : MCP
      American Society for Biochemistry and Molecular Biology
      transmembrane domain, transporter, urea, alkaline, protein complex, protein–protein interaction, ACN, acetonitrile, AMED, Japan Agency for Medical Research and Development, BCA, bicinchoninic acid, FA, formic acid, GO, Gene Ontology, GRAVY, grand average of hydropathy, HEK293T, human embryonic kidney 293T cell line, IPA, ingenuity pathway analysis, MS, mass spectrometry, PPI, protein–protein interaction, PSM, peptide spectra match, SDB-XC, styrene-divinylbenzene crosslinked, SDC, sodium deoxycholate, SLC, solute carrier, STRING, Search Tool for the Retrieval of Interacting Genes/Proteins, TMD, transmembrane domain, Tris, Tris(hydroxymethyl)aminomethane

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          Abstract

          Membrane proteins play essential roles in various cellular processes, such as nutrient transport, bioenergetic processes, cell adhesion, and signal transduction. Proteomics is one of the key approaches to exploring membrane proteins comprehensively. Bottom–up proteomics using LC–MS/MS has been widely used in membrane proteomics. However, the low abundance and hydrophobic features of membrane proteins, especially integral membrane proteins, make it difficult to handle the proteins and are the bottleneck for identification by LC–MS/MS. Herein, to improve the identification and quantification of membrane proteins, we have stepwisely evaluated methods of membrane enrichment for the sample preparation. The enrichment methods of membranes consisted of precipitation by ultracentrifugation and treatment by urea or alkaline solutions. The best enrichment method in the study, washing with urea after isolation of the membranes, resulted in the identification of almost twice as many membrane proteins compared with samples without the enrichment. Notably, the method significantly enhances the identified numbers of multispanning transmembrane proteins, such as solute carrier transporters, ABC transporters, and G-protein–coupled receptors, by almost sixfold. Using this method, we revealed the profiles of amino acid transport systems with the validation by functional assays and found more protein–protein interactions, including membrane protein complexes and clusters. Our protocol uses standard procedures in biochemistry, but the method was efficient for the in-depth analysis of membrane proteome in a wide range of samples.

          Graphical Abstract

          Highlights

          • Fractionation of membranes improves the identification of membrane proteins.

          • Membranes washed with urea or alkaline increase identified transmembrane proteins.

          • Urea wash increases the detection of multispanning transmembrane proteins.

          • Proteomics of urea-washed membranes keeps more protein–protein interactions.

          In Brief

          Membrane proteins, particularly integral membrane proteins, are barely detected in bottom–up proteomics because of their complex nature and abundant soluble proteins. We applied standard biochemical procedures to optimize the sample preparation method for membrane proteome. Membranes were precipitated by ultracentrifugation, followed by treatment with urea or alkaline solutions to remove contaminants. This enrichment was critical to obtain comprehensive membrane proteome data. Among the methods, washing membranes by urea distinctly revealed intricate membrane proteome with keeping protein–protein interactions.

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          Most cited references75

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              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/.
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                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
                25 January 2022
                May 2022
                25 January 2022
                : 21
                : 5
                : 100206
                Affiliations
                [1 ]Department of Laboratory Medicine, The Jikei University School of Medicine, Tokyo, Japan
                [2 ]Department of Collaborative Research for Biomolecular Dynamics, Nara Medical University, Nara, Japan
                [3 ]Department of Bio-system Pharmacology, Graduate School of Medicine, Osaka University, Osaka, Japan
                Author notes
                []For correspondence: Shushi Nagamori snagamori@ 123456nagamori-lab.jp
                Article
                S1535-9476(22)00014-7 100206
                10.1016/j.mcpro.2022.100206
                9062332
                35085786
                57b712f5-72f0-44c5-af4a-70465ee97a1e
                © 2022 The Authors

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

                History
                : 21 July 2021
                : 13 December 2021
                Categories
                Research

                Molecular biology
                transmembrane domain,transporter,urea,alkaline,protein complex,protein–protein interaction,acn, acetonitrile,amed, japan agency for medical research and development,bca, bicinchoninic acid,fa, formic acid,go, gene ontology,gravy, grand average of hydropathy,hek293t, human embryonic kidney 293t cell line,ipa, ingenuity pathway analysis,ms, mass spectrometry,ppi, protein–protein interaction,psm, peptide spectra match,sdb-xc, styrene-divinylbenzene crosslinked,sdc, sodium deoxycholate,slc, solute carrier,string, search tool for the retrieval of interacting genes/proteins,tmd, transmembrane domain,tris, tris(hydroxymethyl)aminomethane

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