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      Quantitative Proteomics Reveals Protein–Protein Interactions with Fibroblast Growth Factor 12 as a Component of the Voltage-Gated Sodium Channel 1.2 (Nav1.2) Macromolecular Complex in Mammalian Brain*

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          Abstract

          Voltage-gated sodium channels (Nav1.1–Nav1.9) are responsible for the initiation and propagation of action potentials in neurons, controlling firing patterns, synaptic transmission and plasticity of the brain circuit. Yet, it is the protein–protein interactions of the macromolecular complex that exert diverse modulatory actions on the channel, dictating its ultimate functional outcome. Despite the fundamental role of Nav channels in the brain, information on its proteome is still lacking. Here we used affinity purification from crude membrane extracts of whole brain followed by quantitative high-resolution mass spectrometry to resolve the identity of Nav1.2 protein interactors. Of the identified putative protein interactors, fibroblast growth factor 12 (FGF12), a member of the nonsecreted intracellular FGF family, exhibited 30-fold enrichment in Nav1.2 purifications compared with other identified proteins. Using confocal microscopy, we visualized native FGF12 in the brain tissue and confirmed that FGF12 forms a complex with Nav1.2 channels at the axonal initial segment, the subcellular specialized domain of neurons required for action potential initiation. Co-immunoprecipitation studies in a heterologous expression system validate Nav1.2 and FGF12 as interactors, whereas patch-clamp electrophysiology reveals that FGF12 acts synergistically with CaMKII, a known kinase regulator of Nav channels, to modulate Nav1.2-encoded currents. In the presence of CaMKII inhibitors we found that FGF12 produces a bidirectional shift in the voltage-dependence of activation (more depolarized) and the steady-state inactivation (more hyperpolarized) of Nav1.2, increasing the channel availability. Although providing the first characterization of the Nav1.2 CNS proteome, we identify FGF12 as a new functionally relevant interactor. Our studies will provide invaluable information to parse out the molecular determinant underlying neuronal excitability and plasticity, and extending the relevance of iFGFs signaling in the normal and diseased brain.

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          Most cited references 56

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          The Proteomics Identifications (PRIDE) database and associated tools: status in 2013

          The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database at the European Bioinformatics Institute is one of the most prominent data repositories of mass spectrometry (MS)-based proteomics data. Here, we summarize recent developments in the PRIDE database and related tools. First, we provide up-to-date statistics in data content, splitting the figures by groups of organisms and species, including peptide and protein identifications, and post-translational modifications. We then describe the tools that are part of the PRIDE submission pipeline, especially the recently developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector (visualization and analysis tool). We also give an update about the integration of PRIDE with other MS proteomics resources in the context of the ProteomeXchange consortium. Finally, we briefly review the quality control efforts that are ongoing at present and outline our future plans.
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            The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

            Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.
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              Analysis of protein complexes using mass spectrometry.

              The versatile combination of affinity purification and mass spectrometry (AP-MS) has recently been applied to the detailed characterization of many protein complexes and large protein-interaction networks. The combination of AP-MS with other techniques, such as biochemical fractionation, intact mass measurement and chemical crosslinking, can help to decipher the supramolecular organization of protein complexes. AP-MS can also be combined with quantitative proteomics approaches to better understand the dynamics of protein-complex assembly.
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                Author and article information

                Journal
                Mol Cell Proteomics
                Mol. Cell Proteomics
                mcprot
                mcprot
                MCP
                Molecular & Cellular Proteomics : MCP
                The American Society for Biochemistry and Molecular Biology
                1535-9476
                1535-9484
                May 2015
                27 February 2015
                27 February 2015
                : 14
                : 5
                : 1288-1300
                Affiliations
                From the ‡Department of Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, Texas, 77555-0617;
                §Neuroscience Graduate Program, Graduate School of Biomedical Sciences, University of Texas Medical Branch, 301 University Blvd., Galveston, Texas, 77555-0617;
                ¶UTMB Cancer Center, University of Texas Medical Branch, 301 University Blvd., Galveston, Texas, 77555-1074;
                ‖Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Blvd., Galveston, Texas, 77555-0617;
                **National Center for Genome Analysis Support, Indiana University, 107 S Indiana Ave., Bloomington, Indiana, 47408
                Author notes
                ‡‡ To whom correspondence should be addressed: Norelle Wildburger, Graduate Program in Neuroscience, Department of Pharmacology and Toxicology, Tel.: 001 (409) 772-4965; E-mail: ncwildbu@ 123456utmb.edu ; Fernanda Laezza, Department of Pharmacology and Toxicology, Tel.: 001 (409) 772-9672; University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555–0617. E-mail: felaezza@ 123456utmb.edu .
                Article
                M114.040055
                10.1074/mcp.M114.040055
                4424400
                25724910
                © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

                Author's Choice—Final version free via Creative Commons CC-BY license.

                Product
                Funding
                Funded by: National Institutes of Health
                Award ID: R01 MH095995-A1
                Categories
                Research

                Molecular biology

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