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  • A Venn diagram representing RNA seq

    2024-04-15

    A. Venn diagram representing RNA-seq data that show the number of gene overlaps in GFP and HepKO livers after chronic PF-06409577 dosing. B. Top canonical pathways that changed according to Ingenuity Pathway Analysis after chronic PF-06409577 dosing. C. Fibrosis-related genes that were altered in GFP livers after PF-06409577 dosing according to Ingenuity Pathway Analysis. D. qPCR data showing the expression of fibrosis-related genes in GFP and HepKO after chronic PF-06409577 dosing. 5–6 individual animals in each dose group were used in RNAseq analysis. 10–11 animals in each dose group were used in the qPCR analysis. 1-way ANOVA was used to evaluate statistical significance, where * denotes p<0.05, ** denotes p<0.01, *** denotes p<0.001, and **** denotes p<0.0001. A. qPCR data showing relative expression levels of lipogenic genes in livers from animals treated chronically with PF-06409577. B. qPCR data showing relative expression levels of mitochondria related transcripts in livers of mice treated chronically with PF-06409577. 10–11 animals in each dose group were used for qPCR analysis. C–E. Proteomic analysis data represented in a volcano plot from HepG2 gamma-Glu-Cys treated vehicle and PF-06409577 overnight, with significant (C) cholesterol biosynthesis, (D) ribosomal/mTOR, and (E) mitochondrial function related protein sets displayed. 1-way ANOVA was used to evaluate statistical significance, where * denotes p<0.05, ** denotes p<0.01, *** denotes p<0.001, and **** denotes p<0.0001. A-D. ZSF-1 obese rats dosed increasing concentrations of PF-06409577. (A) Normalized hepatic phopho-AMPK levels; (B) plasma cholesterol; (C) plasma HDL and (D) liver triglyceride levels. 10–12 rats were in each dose group. E-G. Cynomolgus monkeys were dosed p.o. daily with 25mg/kg PF-06409577. (E) Plasma cholesterol, (F) plasma LDL, and (G) plasma HDL levels were measured periodically and represented as percent change from each animals baseline value (%CFB). 6–10 monkeys were used in each group. 1-way ANOVA was used to evaluate statistical significance, where * denotes p<0.05, ** denotes p<0.01, *** denotes p<0.001, and **** denotes p<0.0001.
    Acknowledgements G.R.S. is supported by a Canada gamma-Glu-Cys Research Chair in Metabolism and Obesity and the J. Bruce Duncan Chair in Metabolic Diseases. Experiments in ACC KI mice were supported by grants from the Canadian Institutes of Health Research (125980-1) to G.R.S and mice were provided by Bruce E. Kemp (St. Vincent's Institute of Medical Research, Melbourne Australia).
    Introduction Uncontrolled inflammation could induce non-specific tissue injury, which represents a principle mechanism underlying the development of various clinical disorders [1]. Under the most serious situation, the dysregulated systemic inflammatory cascades might result in diffused injury and even death [2]. To prevent excessive inflammation-induced injury, the signaling pathways driving the progression of inflammation have been extensively investigated and various preventive approaches have been proposed [3]. Interestingly, recent studies indicated that the active molecular responses in inflammation requires intensive metabolic support and modulation of the metabolic pathways might become a novel strategy to restrict inflammatory injury [4]. Metformin is a representative reagent with broad and strong metabolic regulatory activities, which is widely used as a first-line anti-diabetic drug for the treatment of type 2 diabetes [5,6]. In addition to its well-known hypoglycemic activities, increasing evidence suggest that metformin suppress the expression of pro-inflammatory genes in vitro and alleviated inflammatory injury in vivo [[7], [8], [9], [10], [11], [12]]. The mechanisms underlying the pharmacological effects of metformin largely remain unknown. It has been suggested that metformin suppress the activity of mitochondrial respiratory complex, which decreases the generation of ATP and activates adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) [13]. AMPK is a pivotal metabolic regulator which is activated at low-energy status and plays crucial roles in the maintenance of metabolic homeostasis [14]. Currently, AMPK has been regarded as a major target mediating the effects of metformin [15].