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Compensatory Islet Response to Insulin Resistance Revealed by Quantitative Proteomics
Journal article   Peer reviewed

Compensatory Islet Response to Insulin Resistance Revealed by Quantitative Proteomics

Abdelfattah El Ouaamari, Jian-Ying Zhou, Chong Wee Liew, Jun Shirakawa, Ercument Dirice, Nicholas Gedeon, Sevim Kahrarnan, Dario F. De Jesus, Shweta Bhatt, Jong-Seo Kim, …
Journal of proteome research, Vol.14(8), pp.3111-3122
07/08/2015
PMID: 26151086

Abstract

Biochemical Research Methods Biochemistry & Molecular Biology Life Sciences & Biomedicine Science & Technology
Compensatory islet response is a distinct feature of the prediabetic insulin-resistant state in humans and rodents. To identify alterations in the islet proteome that characterize the adaptive response, we analyzed islets from 5 month old male control, high-fat diet fed (HFD), or obese ob/ob mice by LC-MS/MS and quantified similar to 1100 islet proteins (at least two peptides) with a false discovery rate < 1%. Significant alterations in abundance were observed for similar to 350 proteins among groups. The majority of alterations were common to both models, and the changes of a subset of similar to 40 proteins and 12 proteins were verified by targeted quantification using selected reaction monitoring and western blots, respectively. The insulin-resistant islets in both groups exhibited reduced expression of proteins controlling energy metabolism, oxidative phosphorylation, hormone processing, and secretory pathways. Conversely, an increased expression of molecules involved in protein synthesis and folding suggested effects in endoplasmic reticulum stress response, cell survival, and proliferation in both insulin-resistant models. In summary, we report a unique comparison of the islet proteome that is focused on the compensatory response in two insulin-resistant rodent models that are not overtly diabetic. These data provide a valuable resource of candidate proteins to the scientific community to undertake further studies aimed at enhancing beta-cell mass in patients with diabetes. The data are available via the MassIVE repository, under accession no. MSV000079093.

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