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      Dynamic Adipocyte Phosphoproteome Reveals that Akt Directly Regulates mTORC2

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          Summary

          A major challenge of the post-genomics era is to define the connectivity of protein phosphorylation networks. Here, we quantitatively delineate the insulin signaling network in adipocytes by high-resolution mass spectrometry-based proteomics. These data reveal the complexity of intracellular protein phosphorylation. We identified 37,248 phosphorylation sites on 5,705 proteins in this single-cell type, with approximately 15% responding to insulin. We integrated these large-scale phosphoproteomics data using a machine learning approach to predict physiological substrates of several diverse insulin-regulated kinases. This led to the identification of an Akt substrate, SIN1, a core component of the mTORC2 complex. The phosphorylation of SIN1 by Akt was found to regulate mTORC2 activity in response to growth factors, revealing topological insights into the Akt/mTOR signaling network. The dynamic phosphoproteome described here contains numerous phosphorylation sites on proteins involved in diverse molecular functions and should serve as a useful functional resource for cell biologists.

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          Highlights

          • MS/MS identified >37,000 phosphorylation sites in adipocytes

          • Insulin regulates the phosphoproteome over a wide temporal timescale

          • Akt phosphorylates SIN1 on T86 in response to insulin

          • SIN1 phosphorylation activates a positive feedback loop between Akt and mTORC2

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          Author and article information

          Contributors
          Journal
          Cell Metab
          Cell Metab
          Cell Metabolism
          Cell Press
          1550-4131
          1932-7420
          04 June 2013
          04 June 2013
          : 17
          : 6
          : 1009-1020
          Affiliations
          [1 ]Diabetes and Obesity Program, Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia
          [2 ]Biological Mass Spectrometry Unit, Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia
          [3 ]School of Mathematics and Statistics, University of Sydney, Camperdown, New South Wales 2006, Australia
          Author notes
          [* ]Corresponding author d.james@ 123456garvan.org.au
          Article
          CMET1313
          10.1016/j.cmet.2013.04.010
          3690479
          23684622
          a34bf568-7088-48cf-a649-f635e16c9282
          © 2013 ELL & Excerpta Medica.

          This document may be redistributed and reused, subject to certain conditions.

          History
          : 26 November 2012
          : 15 March 2013
          : 17 April 2013
          Categories
          Resource

          Cell biology
          Cell biology

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