Disorders of hepatic energy metabolism, which can be regulated by endoplasmic reticulum (ER) stress, lead to metabolic diseases such as hepatic steatosis and hypoglycemia. Tunicamycin, a pharmacological ER stress inducer, is used to develop an anti-cancer drug. However, the effects of tunicamycin on hepatic energy metabolism have not been well elucidated. Mice were intraperitoneally injected with tunicamycin or vehicle. Twenty-four hours later, hepatic triglyceride and glycogen content and serum lipids profiles were analyzed, as well as the expression of lipogenic and gluconeogenic genes. Tunicamycin significantly induced hepatic a yellowish color and ER stress, as well as increasing serum levels of aspartate transaminase and alanine transaminase. Besides, tunicamycin remarkably increased hepatic triglyceride content and suppressed the expression of apolipoprotein B100. In addition, tunicamycin-treated mice had lower serum levels of triglyceride, apolipoprotein B, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol. Gene expression of peroxisome proliferator-activated receptor α was decreased by tunicamycin, but the protein level was increased. Furthermore, blood glucose level and hepatic glycogen content were decreased in tunicamycin-treated mice. Protein kinase B signaling was attenuated in the tunicamycin-treated liver, but the expression and activities of phosphoenolpyruvate carboxykinase and glucose-6-phosphatase were unchanged. Tunicamycin alters hepatic energy homeostasis by increasing triglyceride accumulation and decreasing glycogen content.
Pubmed ID: 28777337 RIS Download
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View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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