Large-Scale Proteomic Analysis of Patients with Type 2 Diabetes Mellitus and Atherosclerosis Using a Label-Free LC-MS/MS Approach

Authors : Mustafa Gani SÜRMEN, Tijen ALKAN BOZKAYA, Şanser ATEŞ, Saime SÜRMEN, Çağrı ÇAKICI, Sadrettin PENÇE, Neslin EMEKLİ

Pages : 26-38

Doi:10.26650/experimed.1219362

View : 24 | Download : 8

Publication Date : 2023-05-11

Article Type : Research Paper

Abstract :Objective: Type 2 diabetes mellitus insert ignore into journalissuearticles values(T2D); is a metabolic disease whose molecular events have not yet been fully clarified. However, next-generation powerful molecular approaches such as mass spectrometry insert ignore into journalissuearticles values(MS);-based proteomics holds promise. In this study, we aimed to reveal the protein profile of serum samples obtained from patients with T2D and atherosclerotic cardiovascular disease using the high-resolution liquid chromatography insert ignore into journalissuearticles values(LC);-MS/MS system. Materials and Methods: Immune depletion was performed for the top 12 abundant proteins in 10 μl serum samples taken from individuals. Then, tryptic peptides were obtained from total proteins by applying a digestion protocol. Accordingly, reduction, alkylation, and digestion with trypsin enzyme were carried out, respectively. Tryptic peptides were analyzed in an ultra-high-pressure LC-MS/MS system with a label-free proteomic approach. The raw data were processed using the software program. Results: LC-MS/MS analyses revealed 120 proteins with significant expression changes. Some of these proteins were associated with inflammation, lipid transport, and oxidative stress, which are known to play an important role in T2D and its complications. Conclusion: As a result, LC-MS/MS analyses highlighted the proteins that will provide predictions in the treatment and course of T2D. We believe that validation of these proteins with targeted proteomic approaches in a larger sample in further studies will contribute to the development of clinically usable panels.
Keywords : Diabetes, Proteomics, Serum, Mass spectrometry, Atherosclerosis

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