Extracting Time-dependent Obese-diabetic Specific Networks in Hepatic Proteome Analysis
datasetposted on 07.12.2012, 00:00 by Mukul K. Midha, Kamiya Tikoo, Neeraj Sinha, Simarjeet Kaur, Hirdya N. Verma, Kanury V. S. Rao, Samrat Chatterjee, Venkatasamy Manivel
Molecular mechanism governing biological processes leading to dietary obesity and diabetes are largely unknown. Here we study the liver proteome differentially expressed in a long-term high-fat and high-sucrose diet (HFHSD)-induced obesity and diabetes mouse model. Changes in mouse liver proteins were identified using iTRAQ, offline 2D LC (SCX and RP) and MALDI-TOF/TOF MS. A total of 1639 proteins was quantified during 3–15 weeks of disease progression and a pronounced proteome change was captured by incorporating the statistical analysis and network analysis. This underscores the importance of protein expression profiles involved in different biological processes that correlate well with the disease progression. The functionally important modules with key hub proteins such as Egfr, Pklr, Suclg1, and Pcx (Carbohydrate metabolism), Cyp2e1, Fasn, Acat1, and Hmgcs2 (Lipid metabolism and ketogenesis), and Gpx1, Mgst1, and Sod2 (ROS metabolism) can be linked to a physiological state of obesity and T2D. Multiple proteins involved in glucose catabolism and lipogenesis were down-regulated, whereas proteins involved in lipid peroxidation and oxidative phosphorylation were up-regulated. In conclusion, this proteomic study provides targets for future mechanistic and therapeutic studies in relation to development and prevention of obesity and Type 2 Diabetes.