Summary AMPK has emerged as a critical mechanism for salutary effects of polyphenols on lipid metabolic disorders in type 1 and type 2 diabetes. We demonstrate that AMPK interacts with and directly phosphorylates sterol regulatory element binding proteins (SREBP-1c and −2). Ser372 phosphorylation of SREBP-1c by AMPK is sufficient and necessary for inhibition of proteolytic processing and transcriptional activity of SREBP-1c in response to polyphenols and metformin. AMPK stimulates Ser372 phosphorylation, suppresses SREBP-1c cleavage and nuclear translocation, and represses SREBP-1c target gene expression in hepatocytes exposed to high glucose, leading to reduced lipogenesis and lipid accumulation. Hepatic activation of AMPK by the synthetic polyphenol S17834 protects against hepatic steatosis, hyperlipidemia, and accelerated atherosclerosis in diet-induced insulin resistant LDL receptor deficient mice in part through phosphorylation of SREBP-1c Ser372 and suppression of SREBP-1c and −2-dependent lipogenesis. AMPK-dependent phosphorylation of SREBP may offer novel therapeutic strategies to combat insulin resistance, dyslipidemia, and atherosclerosis.

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In this paper, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the problem addressed is to derive some easy-to-test conditions such that the dynamics of the neural network is stochastically exponentially stable in the mean square, independent of the time delay. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions, and therefore the global exponential stability in the mean square for the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.

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