Secretome Analysis of Lipid-Induced Insulin Resistance in Skeletal Muscle Cells by a Combined Experimental and Bioinformatics Workflow
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, i...
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| Veröffentlicht in: | Journal of proteome research Jg. 14; H. 11; S. 4885 - 4895 |
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| Sprache: | Englisch |
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American Chemical Society
06.11.2015
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| ISSN: | 1535-3893, 1535-3907, 1535-3907 |
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| 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, ∼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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| AbstractList | 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. 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.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. |
| Author | Deshmukh, Atul S Mann, Matthias Jensen, Lars Juhl Meissner, Felix Cox, Juergen |
| AuthorAffiliation | Max-Planck-Institute of Biochemistry The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences University of Copenhagen Department of Proteomics and Signal Transduction |
| AuthorAffiliation_xml | – name: Max-Planck-Institute of Biochemistry – name: Department of Proteomics and Signal Transduction – name: University of Copenhagen – name: The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26457550$$D View this record in MEDLINE/PubMed |
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| Keywords | insulin signaling glucose uptake mass spectrometry myokines metabolism secretome diabetes palmitic acid quantitative proteomics obesity |
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| SubjectTerms | Amino Acid Sequence Animals Cell Differentiation Cell Line Computational Biology - methods Cytokines - genetics Cytokines - isolation & purification Cytokines - secretion Gene Expression Regulation Glucose - metabolism Glucose - pharmacology Insulin - metabolism Insulin - pharmacology Insulin Resistance Insulin-Like Growth Factor Binding Proteins - genetics Insulin-Like Growth Factor Binding Proteins - isolation & purification Insulin-Like Growth Factor Binding Proteins - secretion Intercellular Signaling Peptides and Proteins - genetics Intercellular Signaling Peptides and Proteins - isolation & purification Intercellular Signaling Peptides and Proteins - secretion Metalloproteases - genetics Metalloproteases - isolation & purification Metalloproteases - secretion Mice Molecular Sequence Data Muscle Fibers, Skeletal - cytology Muscle Fibers, Skeletal - drug effects Muscle Fibers, Skeletal - secretion Myoblasts - cytology Myoblasts - drug effects Myoblasts - secretion Palmitic Acid - pharmacology Protein Sorting Signals - genetics Protein Structure, Tertiary Serum Albumin, Bovine - chemistry |
| Title | Secretome Analysis of Lipid-Induced Insulin Resistance in Skeletal Muscle Cells by a Combined Experimental and Bioinformatics Workflow |
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