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  • 标题:Using SRM-MS to quantify nuclear protein abundance differences between adipose tissue depots of insulin-resistant mice
  • 本地全文:下载
  • 作者:Asuka Ota ; Kyle M. Kovary ; Olivia H. Wu
  • 期刊名称:JLR Papers In Press
  • 印刷版ISSN:0022-2275
  • 电子版ISSN:1539-7262
  • 出版年度:2015
  • 卷号:56
  • 期号:5
  • 页码:1068-1078
  • DOI:10.1194/jlr.D056317
  • 语种:English
  • 出版社:American Society for Biochemistry and Molecular Biology
  • 摘要:Insulin resistance (IR) underlies metabolic disease. Visceral, but not subcutaneous, white adipose tissue (WAT) has been linked to the development of IR, potentially due to differences in regulatory protein abundance. Here we investigate how protein levels are changed in IR in different WAT depots by developing a targeted proteomics approach to quantitatively compare the abundance of 42 nuclear proteins in subcutaneous and visceral WAT from a commonly used insulin-resistant mouse model, Lepr(db/db), and from C57BL/6J control mice. The most differentially expressed proteins were important in adipogenesis, as confirmed by siRNA-mediated depletion experiments, suggesting a defect in adipogenesis in visceral, but not subcutaneous, insulin-resistant WAT. Furthermore, differentiation of visceral, but not subcutaneous, insulin-resistant stromal vascular cells (SVCs) was impaired. In an in vitro approach to understand the cause of this impaired differentiation, we compared insulin-resistant visceral SVCs to preadipocyte cell culture models made insulin resistant by different stimuli. The insulin-resistant visceral SVC protein abundance profile correlated most with preadipocyte cell culture cells treated with both palmitate and TNFα. Together, our study introduces a method to simultaneously measure and quantitatively compare nuclear protein expression patterns in primary adipose tissue and adipocyte cell cultures, which we show can reveal relationships between differentiation and disease states of different adipocyte tissue types.
  • 关键词:adipocytes ; peroxisome proliferator-activated receptors ; obesity ; insulin resistance ; quantitative proteomics ; selected reaction monitoring mass spectrometry
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