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

Deshmukh, A. S., Cox, J., Jensen, L. J., Meissner, F., & Mann, M. (2015). Secretome Analysis of Lipid-Induced Insulin Resistance in Skeletal Muscle Cells by a Combined Experimental and Bioinformatics Workflow. JOURNAL OF PROTEOME RESEARCH, 14(11), 4885-4895. doi:10.1021/acs.jproteome.5b00720.

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Deshmukh, Atul S.1, Author              
Cox, Juergen2, Author              
Jensen , Lars Juhl3, Author
Meissner, Felix4, Author              
Mann, Matthias1, Author              
Affiliations:
1Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              
2Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              
3external, ou_persistent22              
4Meissner, Felix / Experimental Systems Immunology, Max Planck Institute of Biochemistry, Max Planck Society, ou_2149678              

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Free keywords: TUMOR-NECROSIS-FACTOR; L6 MYOTUBE SECRETOME; PROTEIN-KINASE-C; GROWTH-FACTOR-I; PROTEOMIC ANALYSIS; NEUROTROPHIC FACTOR; DIABETES-MELLITUS; BINDING-PROTEINS; FATTY-ACIDS; ACTIVATIONmyokines; quantitative proteomics; mass spectrometry; metabolism; glucose uptake; insulin signaling diabetes; obesity; secretome; palmitic acid;
 Abstract: 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, similar to 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.

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Language(s): eng - English
 Dates: 2015
 Publication Status: Published in print
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: JOURNAL OF PROTEOME RESEARCH
Source Genre: Journal
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Publ. Info: 1155 16TH ST, NW, WASHINGTON, DC 20036 USA : AMER CHEMICAL SOC
Pages: - Volume / Issue: 14 (11) Sequence Number: - Start / End Page: 4885 - 4895 Identifier: ISSN: 1535-3893