TY - JOUR

T1 - Secretome Analysis of Lipid-Induced Insulin Resistance in Skeletal Muscle Cells by a Combined Experimental and Bioinformatics Workflow

AU - Deshmukh, Atul S

AU - Cox, Juergen

AU - Jensen, Lars Juhl

AU - Meissner, Felix

AU - Mann, Matthias

PY - 2015/11/6

Y1 - 2015/11/6

N2 - 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.

AB - 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.

U2 - 10.1021/acs.jproteome.5b00720

DO - 10.1021/acs.jproteome.5b00720

M3 - Journal article

C2 - 26457550

VL - 14

SP - 4885

EP - 4895

JO - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 11

ER -