Title : Using proteomics to discover novel biomarkers for fatty liver development and response to CB1R antagonist treatment in an obese mouse model - Chen_2017_Proteomics_17_ |
Author(s) : Chen CC , Lee TY , Kwok CF , Hsu YP , Shih KC , Lin YJ , Ho LT |
Ref : Proteomics , 17 : , 2017 |
Abstract :
Over activity of cannabinoid receptor type 1 (CB1R) plays a key role in increasing the incidence of obesity-induced non-alcoholic fatty liver disease. Tissue proteome analysis has been applied to investigate the bioinformatics regarding the mode of action and therapeutic mechanism. The aim of this study was to explore the potential pathways altered with CB1R in obesity-induced fatty liver. Male C57BL/6 mice were fed either a standard chow diet (STD) or a high-fat diet (HFD) with or without 1-week treatment of CB1R inverse agonist AM251 at 5 mg/kg. Then, liver tissues were harvested for 2DE analysis and protein profiles were identified by using MALDI-MS. Results showed that eight of significantly altered protein spots at the level of changes > twofold were overlapped among the three groups, naming major urinary protein 1, ATP synthase subunit beta, glucosamine-fructose-6-phosphate aminotransferase 1, zine finger protein 2, s-adenosylmethionine synthase isoform type-1, isocitrate dehydrogenase subunit alpha, epoxide hydrolase 2 and 60S acidic ribosomal protein P0. These identified proteins were involved in glucose/lipid metabolic process, xenobiotic metabolic system, and ATP synthesized process in mitochondria. Based on the findings, we speculated that CB1R blockade might exert its anti-metabolic disorder effect via improvement of mitochondrial function in hepatic steatosis in HFD condition. |
PubMedSearch : Chen_2017_Proteomics_17_ |
PubMedID: 27928909 |
Chen CC, Lee TY, Kwok CF, Hsu YP, Shih KC, Lin YJ, Ho LT (2017)
Using proteomics to discover novel biomarkers for fatty liver development and response to CB1R antagonist treatment in an obese mouse model
Proteomics
17 :
Chen CC, Lee TY, Kwok CF, Hsu YP, Shih KC, Lin YJ, Ho LT (2017)
Proteomics
17 :