Select conditions below to toggle them from the plot:
GROUP | CONDITION | SAMPLES |
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mouse liver tissue |
GSM3022546 GSM3022547 GSM3022548 GSM3022549
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GSM3022556 GSM3022557 GSM3022558 GSM3022559 GSM3022560
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GSM3022550 GSM3022551 GSM3022552 GSM3022553 GSM3022554 GSM3022555
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GSM3022561 GSM3022562 GSM3022563 GSM3022564 GSM3022565 GSM3022566
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Submission Date: Feb 26, 2018
Summary: Statins are efficient in preventing cardiovascular disease progression, but their effects in the absence of low density lipoprotein receptor (LDLR) and on the risk of diabetes are not yet well characterized. The aim was to clarify systemic and pleiotropic effects of Rosuvastatin on cardiovascular and diabetic phenotypes in hypercholesterolemic prediabetic mice. The IGF-II/LDLR-/-ApoB100/100 animals were tested for Rosuvastatin effects on plasma glucose, insulin, lipids, atherosclerosis and liver steatosis. To achieve a more comprehensive view, genes differentially expressed in response to Rosuvastatin were identified by RNA sequencing in liver tissue. Rosuvastatin significantly reduced plasma cholesterol in hypercholesterolemic LDLR – deficient mice, but had no effects on atherosclerosis development at aortic sinus level and in coronary arteries. Rosuvastatin also significantly reduced liver steatosis without any pronounced effects on glucose or insulin levels. RNA sequencing revealed that Rosuvastatin – responsive hepatic genes are involved in cholesterol metabolism and in anti-inflammatory response. In addition, significant changes were detected in the expression of Perilipin 4 and 5 which are involved in lipid droplet formation in the liver. For the first time it was shown that Tribbles proteins are affected by Rosuvastatin treatment in the hypercholesterolemic mice. In conclusion, Rosuvastatin treatment had several positive effects in hypercholesterolemic mice showing early signs of diabetes, and many of these outcomes are unrelated to direct effects on cholesterol and lipoprotein metabolism. These results increase our understanding about the systemic and pleiotropic effects of Rosuvastatin in the absence of LDLR expression.
GEO Accession ID: GSE111125
PMID: 30508567
Submission Date: Feb 26, 2018
Summary: Statins are efficient in preventing cardiovascular disease progression, but their effects in the absence of low density lipoprotein receptor (LDLR) and on the risk of diabetes are not yet well characterized. The aim was to clarify systemic and pleiotropic effects of Rosuvastatin on cardiovascular and diabetic phenotypes in hypercholesterolemic prediabetic mice. The IGF-II/LDLR-/-ApoB100/100 animals were tested for Rosuvastatin effects on plasma glucose, insulin, lipids, atherosclerosis and liver steatosis. To achieve a more comprehensive view, genes differentially expressed in response to Rosuvastatin were identified by RNA sequencing in liver tissue. Rosuvastatin significantly reduced plasma cholesterol in hypercholesterolemic LDLR – deficient mice, but had no effects on atherosclerosis development at aortic sinus level and in coronary arteries. Rosuvastatin also significantly reduced liver steatosis without any pronounced effects on glucose or insulin levels. RNA sequencing revealed that Rosuvastatin – responsive hepatic genes are involved in cholesterol metabolism and in anti-inflammatory response. In addition, significant changes were detected in the expression of Perilipin 4 and 5 which are involved in lipid droplet formation in the liver. For the first time it was shown that Tribbles proteins are affected by Rosuvastatin treatment in the hypercholesterolemic mice. In conclusion, Rosuvastatin treatment had several positive effects in hypercholesterolemic mice showing early signs of diabetes, and many of these outcomes are unrelated to direct effects on cholesterol and lipoprotein metabolism. These results increase our understanding about the systemic and pleiotropic effects of Rosuvastatin in the absence of LDLR expression.
GEO Accession ID: GSE111125
PMID: 30508567
Signatures:
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This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.