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J Proteome Res. 2015 Nov 6;14(11):4885-95. doi: 10.1021/acs.jproteome.5b00720. Epub 2015 Oct 28.

Secretome Analysis of Lipid-Induced Insulin Resistance in Skeletal Muscle Cells by a Combined Experimental and Bioinformatics Workflow.

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Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry , Am Klopferspitz 18, D-82152 Martinsried, Germany.
The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Building 6.1, DK-2200 Copenhagen, Denmark.


Skeletal muscle has emerged as an important secretory organ that produces so-called myokines, regulating energy metabolism via autocrine, paracrine, and endocrine actions; however, the nature and extent of the muscle secretome has not been fully elucidated. Mass spectrometry (MS)-based proteomics, in principle, allows an unbiased and comprehensive analysis of cellular secretomes; however, the distinction of bona fide secreted proteins from proteins released upon lysis of a small fraction of dying cells remains challenging. Here we applied highly sensitive MS and streamlined bioinformatics to analyze the secretome of lipid-induced insulin-resistant skeletal muscle cells. Our workflow identified 1073 putative secreted proteins including 32 growth factors, 25 cytokines, and 29 metalloproteinases. In addition to previously reported proteins, we report hundreds of novel ones. Intriguingly, ∼40% of the secreted proteins were regulated under insulin-resistant conditions, including a protein family with signal peptide and EGF-like domain structure that had not yet been associated with insulin resistance. Finally, we report that secretion of IGF and IGF-binding proteins was down-regulated under insulin-resistant conditions. Our study demonstrates an efficient combined experimental and bioinformatics workflow to identify putative secreted proteins from insulin-resistant skeletal muscle cells, which could easily be adapted to other cellular models.


diabetes; glucose uptake; insulin signaling; mass spectrometry; metabolism; myokines; obesity; palmitic acid; quantitative proteomics; secretome

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